lab7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you turn this lab in, make sure everything runs as expected. **Restart and run all cells** (in the menubar, select Kernel$\\rightarrow$Restart & Run All) then check the output is as expected and there are no errors.  Also remember to **Save** before uploading this file (File$\\rightarrow$Save and Checkpoint)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div align=\"center\">Quantum Computer Systems Design</div>&nbsp;  <div align=\"center\">Lab 7</div>\n",
    "\n",
    "This lab will be very similar to the previous lab, except now you will account for error and variation in the hardware. In the interest of time you will only modify your scheduling pass to account for two things:\n",
    "\n",
    "- Different coherence (T1) times of each qubit\n",
    "- Crosstalk Error\n",
    "\n",
    "Your objective will also be slightly different - last time your goal was to minimize the total execution time of the program and then to minimize total qubit use. Now we want to minimize the effect of decoherence and cross talk so we will be approximating success rate and the goal is to maximize this. You will be given programs which have been mapped and routed (you are welcome to use your own programs for testing).\n",
    "\n",
    "The setup code is the same as before, so feel free to skip to part 0.\n",
    "\n",
    "# 0.1. Programs For Development\n",
    "\n",
    "Below is the same code from last lab which generates the Cuccarro Adder. You're welcome to use this circuit, which has been decomposed into 1 and 2 qubit gates, or any other as you develop your compilation passes. You can adjust the size of the below program `qiskit.transpile(generate_adder_circuit(4), basis_gates=['cx', 'rx', 'h', 'rz'])` by changing the `4` to some other even number for larger circuits. We suggest testing on small circuits however. Even simpler circuits containing only a small number of CNOTs may be beneficial. The grader tests might use programs you have not seen before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import itertools\n",
    "import networkx as nx\n",
    "import qiskit\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "from qiskit.circuit.quantumregister import Qubit\n",
    "from IPython.display import display\n",
    "\n",
    "from typing import Dict, Any, List, Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cuccaro Adder:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 956.385x702.333 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7143.61x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A test program for you to use during development.\n",
    "\n",
    "# Implementation of the Cuccaro Quantum Adder from\n",
    "#   https://github.com/jmbaker94/quantumcircuitbenchmarks\n",
    "# Design from the paper\n",
    "#   https://arxiv.org/abs/quant-ph/0410184\n",
    "\n",
    "# Majority gate\n",
    "maj_c = QuantumCircuit(3, name='MAJ')\n",
    "maj_c.cx(2, 1)\n",
    "maj_c.cx(2, 0)\n",
    "maj_c.ccx(0, 1, 2)\n",
    "maj = maj_c.to_gate(label='MAJ')\n",
    "\n",
    "# 2-CNOT version of UnMajority and Add gate\n",
    "uma2_c = QuantumCircuit(3, name='UMA2')\n",
    "uma2_c.toffoli(0, 1, 2)\n",
    "uma2_c.cx(2, 0)\n",
    "uma2_c.cx(0, 1)\n",
    "uma2 = uma2_c.to_gate(label='UMA2')\n",
    "\n",
    "# 3-CNOT version of UnMajority and Add gate\n",
    "# (Allows more parallelism in the circuit)\n",
    "uma3_c = QuantumCircuit(3, name='UMA3')\n",
    "uma3_c.x(1)\n",
    "uma3_c.cx(0, 1)\n",
    "uma3_c.toffoli(0, 1, 2)\n",
    "uma3_c.x(1)\n",
    "uma3_c.cx(2, 0)\n",
    "uma3_c.cx(2, 1)\n",
    "uma3 = uma3_c.to_gate(label='UMA3')\n",
    "\n",
    "def cuccaro_adder(c, cin, a, b, cout, uma=uma3):\n",
    "    c.append(maj, [cin, b[0], a[0]])\n",
    "    for i in range(1, len(b)):\n",
    "        c.append(maj, [a[i-1], b[i], a[i]])\n",
    "\n",
    "    c.cx(a[-1], cout)\n",
    "\n",
    "    for i in reversed(range(1, len(b))):\n",
    "        c.append(uma, [a[i-1], b[i], a[i]])\n",
    "    c.append(uma, [cin, b[0], a[0]])\n",
    "\n",
    "def generate_adder_circuit(n, uma=uma3):\n",
    "    if n % 2 != 0:\n",
    "        raise ValueError('Odd number of qubits')\n",
    "\n",
    "    qubits = QuantumRegister\n",
    "    cin = range(1)\n",
    "    a = range(1, n//2)\n",
    "    b = range(n//2, n-1)\n",
    "    cout = range(n-1, n)\n",
    "    c = QuantumCircuit(n)\n",
    "        \n",
    "    cuccaro_adder(c, cin, a, b, cout, uma=uma)\n",
    "    return c\n",
    "\n",
    "def decomposed_adder_circuit(n):\n",
    "    return qiskit.transpile(generate_adder_circuit(n),\n",
    "                            basis_gates=['cx', 'rx', 'h', 'rz'])\n",
    "\n",
    "print('Cuccaro Adder:')\n",
    "display(generate_adder_circuit(8).draw(fold=-1))\n",
    "cuccaro = decomposed_adder_circuit(8)\n",
    "cuccaro.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0.2. Data Structures and Iterating through Qiskit Programs\n",
    "\n",
    "Before you get started, let's build some basic data structures you may need for your passes. These objects are for your use, but you do not have to use them in your implementation of the below passes. You may want to modify the data structures to account for other parameters, such as gate times. It may be helpful to read through these functions to see how to manipulate `qiskit.QuantumCircuit`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is an RZ gate\n"
     ]
    }
   ],
   "source": [
    "# 0.2.0 Wrapping Gates to Make them hashable in a graph structure and give them a unique identifying label\n",
    "import dataclasses\n",
    "@dataclasses.dataclass\n",
    "class GateWrapper:\n",
    "    gate:Any\n",
    "    qubits:List[Qubit]\n",
    "    extra_params:List[Any]\n",
    "    label:str\n",
    "        \n",
    "    def __hash__(self):\n",
    "        return hash((type(self.gate), tuple(self.qubits), self.label))\n",
    "    \n",
    "    def __str__(self):\n",
    "        args = ','.join(str(q.index) for q in self.qubits)\n",
    "        return f'{self.label}{{{self.gate.name}({args})}}'\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return str(self)\n",
    "\n",
    "# 0.2.1 Building a new circuit from an old circuit (This is just to see how to use gates from old circuits)\n",
    "def copy_a_circuit(old_circuit, n):\n",
    "    '''\n",
    "    QuantumCircuit's operate on registers of qubits. If the number of qubits in old_circuit is different from the number of qubits in the new circuit \n",
    "    you need to convert the qubits from old to new. \n",
    "\n",
    "    This function explains how to copy gates from one set of qubits to another in a different circuit.\n",
    "\n",
    "    Args: \n",
    "        old_circuit: the circuit to copy\n",
    "        n: number of qubits in the new circuit\n",
    "            \n",
    "    '''\n",
    "    new_circuit = qiskit.QuantumCircuit(n)\n",
    "    \n",
    "    for gate in old_circuit:\n",
    "        # If n = number of qubits in old_circuit this is O.K.\n",
    "        # new_circuit.append(gate[0], gate[1], gate[2])\n",
    "        \n",
    "        # If n != number of qubits in old_circuit the above is NOT O.K.\n",
    "        # Instead, convert the indices\n",
    "        # DO NOT use both this and the above. \n",
    "        new_qubit_indices = [gate[1][i].index for i in range(len(gate[1]))]\n",
    "        new_circuit.append(gate[0], new_qubit_indices, gate[2])\n",
    "        \n",
    "    return new_circuit\n",
    "\n",
    "# 0.2.2 Building a Dependency Graph using NetworkX\n",
    "def build_program_dependency_graph(circuit):\n",
    "    '''\n",
    "    Builds a program dependency graph like in the video. Feel free to modify this or build your own as you see fit.\n",
    "    Here we convert the gates into their hashable versions and add a label.\n",
    "    '''\n",
    "    \n",
    "    # Starting Label Index\n",
    "    i = 0\n",
    "    \n",
    "    # A dictionary to store the last use of any qubit\n",
    "    qubit_last_use = {}\n",
    "    \n",
    "    g = nx.DiGraph()\n",
    "    \n",
    "    # Add the start node\n",
    "    g.add_node(-1)\n",
    "    \n",
    "    for gate in circuit:\n",
    "        # Remember gate is a 3-tuple!\n",
    "        \n",
    "        hashable_gate = GateWrapper(gate[0], gate[1], gate[2], label=i)\n",
    "        i += 1\n",
    "        \n",
    "        g.add_node(hashable_gate)\n",
    "        \n",
    "        # Add edges based on qubit_last_use; update last use\n",
    "        for qubit in hashable_gate.qubits:\n",
    "            if qubit in qubit_last_use:\n",
    "                g.add_edge(qubit_last_use[qubit], hashable_gate)\n",
    "            else:\n",
    "                g.add_edge(-1, hashable_gate)\n",
    "            \n",
    "            qubit_last_use[qubit] = hashable_gate\n",
    "            \n",
    "    # Add the end node\n",
    "    g.add_node(float('inf'))\n",
    "    \n",
    "    for qubit in qubit_last_use:\n",
    "        g.add_edge(qubit_last_use[qubit], float('inf'))\n",
    "            \n",
    "    return g\n",
    "\n",
    "# 0.2.3 From Dependency Graph with Hashable Gates to Qiskit Gates\n",
    "def dependency_graph_to_circuit(dep_graph, n):\n",
    "    '''\n",
    "    Takes a dependency graph and the number of qubits n\n",
    "    '''\n",
    "    \n",
    "    circuit = qiskit.QuantumCircuit(n)\n",
    "    \n",
    "    for gate in nx.topological_sort(dep_graph):\n",
    "        \n",
    "        if gate not in [-1, float('inf')]:\n",
    "            circuit.append(gate.gate, gate.qubits, gate.extra_params)\n",
    "            \n",
    "    return circuit\n",
    "\n",
    "# 0.2.4 Interaction Graphs\n",
    "def interaction_graph_from_circuit(circuit):\n",
    "    '''\n",
    "    Builds a weighted interaction graph for a given circuit.\n",
    "\n",
    "    Nodes are qubits\n",
    "    Edges are weighted by the number of times pairs of qubits interact\n",
    "    '''\n",
    "    g = nx.Graph()\n",
    "    \n",
    "    for gate in circuit:\n",
    "        for q in gate[1]:\n",
    "            g.add_node(q)\n",
    "            \n",
    "        for i in range(len(gate[1])):\n",
    "            for j in range(i):\n",
    "                q1 = gate[1][i]\n",
    "                q2 = gate[1][j]\n",
    "                if q1 != q2:\n",
    "                    if (q1, q2) not in g.edges:\n",
    "                        g.add_edge(q1, q2, weight=1)\n",
    "                    else:\n",
    "                        g.edges[q1, q2]['weight'] += 1\n",
    "        \n",
    "    return g\n",
    "\n",
    "# 0.2.5 Checking if a gate is a certain type\n",
    "basic_c = QuantumCircuit(1)\n",
    "basic_c.rz(np.pi / 2, 0)\n",
    "\n",
    "for gate in basic_c:\n",
    "    if isinstance(gate[0], qiskit.circuit.library.standard_gates.rz.RZGate):\n",
    "        print(\"This is an RZ gate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper functions to visualize\n",
    "def draw_interaction_graph(ig):\n",
    "    nx.draw(nx.relabel_nodes(ig, {q: f'{q.register.name}_{q.index}' for q in ig.nodes}),\n",
    "            with_labels=True)\n",
    "\n",
    "def draw_dependency_graph(dep_g):\n",
    "    nx.draw(dep_g, with_labels=True)\n",
    "\n",
    "def draw_hardware_graph(target_hardware):\n",
    "    nx.draw(target_hardware, with_labels=True)\n",
    "\n",
    "def draw_mapping(target_hardware, mapping):\n",
    "    rev_mapping = {hw_q: None for hw_q in target_hardware.nodes}\n",
    "    for k, v in mapping.items():\n",
    "        assert v in rev_mapping, (\n",
    "            'Invalid mapping: cannot map a circuit qubit to a non-existant hardware qubit '\n",
    "            f'({k}->{v} but {v} does not exist)')\n",
    "        assert rev_mapping[v] is None, (\n",
    "            'Invalid mapping: cannot map two circuit qubits to the same qubit on hardware '\n",
    "            f'({rev_mapping[v]}->{v} and {k}->{v})')\n",
    "        rev_mapping[v] = k\n",
    "    nx.draw(nx.relabel_nodes(target_hardware,\n",
    "                             {hw_q: f'''q{getattr(rev_mapping[hw_q], 'index', None)\n",
    "                                         }->{hw_q}'''\n",
    "                              for hw_q in target_hardware.nodes}),\n",
    "            with_labels=True)\n",
    "\n",
    "def draw_routing(routed_circuit):\n",
    "    routed_circuit.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/832072005.py:3: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  nx.draw(nx.relabel_nodes(ig, {q: f'{q.register.name}_{q.index}' for q in ig.nodes}),\n",
      "/tmp/ipykernel_60/832072005.py:3: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  nx.draw(nx.relabel_nodes(ig, {q: f'{q.register.name}_{q.index}' for q in ig.nodes}),\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simple = QuantumCircuit(3)\n",
    "simple.cx(0, 1)\n",
    "simple.cx(1, 0)\n",
    "simple.x(2)\n",
    "\n",
    "ig = interaction_graph_from_circuit(simple)\n",
    "dep_g = build_program_dependency_graph(simple)\n",
    "draw_interaction_graph(ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3593926637.py:14: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  args = ','.join(str(q.index) for q in self.qubits)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_dependency_graph(dep_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/832072005.py:3: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  nx.draw(nx.relabel_nodes(ig, {q: f'{q.register.name}_{q.index}' for q in ig.nodes}),\n",
      "/tmp/ipykernel_60/832072005.py:3: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  nx.draw(nx.relabel_nodes(ig, {q: f'{q.register.name}_{q.index}' for q in ig.nodes}),\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_interaction_graph(interaction_graph_from_circuit(cuccaro))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3593926637.py:14: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  args = ','.join(str(q.index) for q in self.qubits)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_dependency_graph(build_program_dependency_graph(cuccaro))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Noise-Aware Scheduling\n",
    "\n",
    "For simplicity we will assume that gates executed on different qubits / pairs of qubits still execute in the same amount of time. However, the qubits themselves have different T1 times, reported as a dictionary `{hardware_qubit_i: T_1(hardware_qubit_i)}`. We will also assume that crosstalk error for CNOTs is reported as a value between 0 and 1 for pairs of edges. For example the success rate of gates executed in parallel on edge (u, v) and (w, x) could be given as 0.95. \n",
    "\n",
    "We approximate the probability that no coherence error occurs on a given qubit, $q$, with $T1(q)$ as:\n",
    "    $$\n",
    "        \\epsilon_{T1(q)} = e^{-\\text{Qubit Active Time} / {T1(q)}}\n",
    "    $$\n",
    "So minimizing the active time of the qubits is important, and even more so important for those with shorter T1 times.\n",
    "\n",
    "You need to balance this minimization while making sure gates with high crosstalk do not overlap in time. For simplicity we will assume we incur the crosstalk error cost if two gates overlap at all (even partial overlaps). Therefore if $Q$ is our set of qubits and $X$ is the set of gates which occur in parallel with $\\epsilon_{e_1, e_2}$ the crosstalk error of gate executing on hardware edge pair $e_1, e_2$, our goal is to maximize this function:\n",
    "    $$\n",
    "        \\prod_{u \\in Q} \\epsilon_{T1(q)} \\cdot \\prod_{(e_1, e_2) \\in X} \\epsilon_{e_1, e_2}\n",
    "    $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "802645aee0dd88fc6f08c103bf2a15b5",
     "grade": false,
     "grade_id": "1-previous",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "### Optional: Copy your solutions from the previous lab\n",
    "### for mapping and routing\n",
    "\n",
    "def route_circuit(quantum_circuit:QuantumCircuit, target_hardware:nx.Graph,\n",
    "                  mapping:Dict[Qubit, int]) -> QuantumCircuit:\n",
    "    '''\n",
    "    This function should take an input program given as a\n",
    "    qiskit.QuantumCircuit, a target hardware, given as a networkx\n",
    "    graph, and a mapping (the dictionary from part 2) and return a\n",
    "    new QuantumCircuit with additional operations added in.  We expect\n",
    "    you to SWAP there and SWAP back so the mapping need not be modified, \n",
    "    however more efficient implementations are welcome (see the\n",
    "    bonus challenge in part 4).\n",
    "    '''\n",
    "    ### YOUR SOLUTION IN THIS FUNCTION\n",
    "    from qiskit.circuit.library import SwapGate\n",
    "    new_circuit = QuantumCircuit(len(target_hardware.nodes))\n",
    "    short_paths = dict(nx.all_pairs_shortest_path(target_hardware))\n",
    "    #print(short_paths)\n",
    "    # YOUR CODE HERE\n",
    "    for gate in quantum_circuit:\n",
    "        if len(gate[1]) > 1:\n",
    "            HW_index1 = mapping[gate[1][0]]\n",
    "            HW_index2 = mapping[gate[1][1]]\n",
    "            route = short_paths[HW_index1][HW_index2]\n",
    "            route_len = len(route)\n",
    "            if route_len > 2:\n",
    "                for i in range(route_len-2):\n",
    "                    # Create a SWAP gate\n",
    "                    swap_gate = SwapGate()\n",
    "                    # Append the SWAP gate to the existing circuit on qubits 0 and 1\n",
    "                    new_circuit.append(swap_gate, [route[i],route[i+1]])\n",
    "                new_circuit.append(gate[0], [route[route_len-2],route[route_len-1]], gate[2])\n",
    "                for i in range(route_len-2, 0, -1):\n",
    "                    # Create a SWAP gate\n",
    "                    swap_gate = SwapGate()\n",
    "                    # Append the SWAP gate to the existing circuit on qubits 0 and 1\n",
    "                    new_circuit.append(swap_gate, [route[i-1],route[i]])\n",
    "            else:\n",
    "                new_circuit.append(gate[0], [HW_index1,HW_index2], gate[2])\n",
    "        else:\n",
    "            new_circuit.append(gate[0], [mapping[gate[1][0]]], gate[2])\n",
    "    return new_circuit\n",
    "\n",
    "def map_circuit(quantum_circuit:QuantumCircuit, target_hardware:nx.Graph\n",
    "               ) -> Dict[Qubit, int]:\n",
    "    '''\n",
    "    This function should take an input program given as a\n",
    "    qiskit.QuantumCircuit and a target hardware, given as a networkx\n",
    "    graph and return a dictionary {qubit : hardware_id}\n",
    "\n",
    "    The target_hardware graph is an undirected graph with nodes\n",
    "    labeled 0 to n-1 where n is the total number of qubits.\n",
    "\n",
    "    You should make sure there are enough hardware qubits to fit\n",
    "    your program and raise an appropriate error otherwise.\n",
    "    '''\n",
    "    ### YOUR SOLUTION IN THIS FUNCTION\n",
    "    ig = interaction_graph_from_circuit(quantum_circuit)\n",
    "    assert len(ig.nodes) <= len(target_hardware.nodes), 'Not enough qubits in the hardware'\n",
    "\n",
    "    # Dictionary where keys are the index of qiskit qubit objects (type: int)\n",
    "    # and values are target_hardware node ids (type: int)\n",
    "    mapping:Dict[Qubit, int] = {}\n",
    "\n",
    "    # Other temp variables\n",
    "    used_hardware = set()\n",
    "    #a = all_pairs_shortest_path_length(ig)\n",
    "    i=0\n",
    "    node_ids = list(target_hardware.nodes)\n",
    "\n",
    "    for nn in ig.nodes:\n",
    "        mapping[nn] = node_ids[i]\n",
    "        i+=1\n",
    "\n",
    "    return mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e4332b4b28c4aaff7605a3eb964730e8",
     "grade": false,
     "grade_id": "1",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# For simplicity, assume all 1 qubit gates have the same execution duration\n",
    "# Except, assume Z gates are free, i.e. take no time to execute\n",
    "# but you should still schedule Z gates at the appropriate time between other gates\n",
    "\n",
    "# Here is some sample hardware. Feel free to change this for your testing.\n",
    "# This is hardware with a simple linear connectivity 0 - 1 - 2 - ... - n-1\n",
    "# Graphs with nodes numbered 0, 1, ..., n-1:\n",
    "simple_hardware = nx.path_graph(8)\n",
    "grid_hardware = nx.grid_2d_graph(2, 4)\n",
    "grid_hardware = nx.relabel_nodes(grid_hardware, {n: i for i, n in enumerate(grid_hardware.nodes)})  # Relabel with integer indices\n",
    "\n",
    "# Here are some sample gate times. These are in nanoseconds.\n",
    "sample_gate_times = {\n",
    "    '1' : 10.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 100.0,\n",
    "}\n",
    "\n",
    "# Here are some T1 times for the grid hardware above, randomly generated.\n",
    "# Feel free to play around with these.\n",
    "t1_low = 5e4\n",
    "t1_high = 10e4\n",
    "\n",
    "np.random.seed(1)\n",
    "sample_T1_times = {\n",
    "    i: np.random.uniform(t1_low, t1_high)\n",
    "    for i in grid_hardware.nodes\n",
    "}\n",
    "\n",
    "# Here are some sample crosstalk errors for grid_hardware.\n",
    "# These edges are NOT directional.\n",
    "# So if (1, 2), (4, 5) is in the set so is (2, 1), (4, 5) etc.\n",
    "# These pairs will be sorted so you don't have to try every permutation.\n",
    "xtalk_low = 0.9\n",
    "xtalk_high = 0.99\n",
    "\n",
    "np.random.seed(2)\n",
    "sample_xtalk_errors = {tuple(sorted([tuple(sorted(e1)), tuple(sorted(e2))])): v\n",
    "                       for (e1, e2), v in {\n",
    "    (e1, e2): np.random.uniform(xtalk_low, xtalk_high)\n",
    "    # Example:\n",
    "    #((1, 2), (4, 5)) : 0.95,\n",
    "    #((0, 1), (3, 4)) : 0.9,\n",
    "    #((0, 3), (1, 2)) : 0.98,\n",
    "    for e1, e2 in itertools.combinations(grid_hardware.edges, 2)\n",
    "}.items()}\n",
    "def lookup_xtalk_error(error_dict, edge1, edge2):\n",
    "    '''Use this method to lookup the crosstalk error from the above dictionary\n",
    "    for a pair of CNOT gates.'''\n",
    "    edge1 = tuple(sorted(edge1))\n",
    "    edge2 = tuple(sorted(edge2))\n",
    "    edges = tuple(sorted([edge1, edge2]))\n",
    "    return error_dict[edges]\n",
    "\n",
    "\n",
    "def build_weighted_program_dependency_graph(circuit, gate_times:Dict[str, float]):\n",
    "    '''\n",
    "    Builds a program dependency graph like in the video. Feel free to modify this or build your own as you see fit.\n",
    "    Here we convert the gates into their hashable versions and add a label.\n",
    "    '''\n",
    "    \n",
    "    # Starting Label Index\n",
    "    i = 0\n",
    "    \n",
    "    # A dictionary to store the last use of any qubit\n",
    "    qubit_last_use = {}\n",
    "    \n",
    "    g = nx.DiGraph()\n",
    "    \n",
    "    # Add the start node\n",
    "    g.add_node(-1)\n",
    "    timestamp=0.0\n",
    "    for gate in circuit:\n",
    "        # Remember gate is a 3-tuple!\n",
    "        \n",
    "        hashable_gate = GateWrapper(gate[0], gate[1], gate[2], label=i)\n",
    "        i += 1\n",
    "        \n",
    "        if isinstance(gate[0], qiskit.circuit.library.standard_gates.CXGate):\n",
    "            timestamp=gate_times['cx']\n",
    "        else:\n",
    "            if isinstance(gate[0], qiskit.circuit.library.standard_gates.rx.RXGate) or \\\n",
    "            isinstance(gate[0], qiskit.circuit.library.standard_gates.h.HGate) or \\\n",
    "            isinstance(gate[0], qiskit.circuit.library.standard_gates.u3.U3Gate):\n",
    "                timestamp = gate_times['1']\n",
    "            else: timestamp=gate_times['rz']\n",
    "        \n",
    "        g.add_node(hashable_gate)\n",
    "\n",
    "        # Add edges based on qubit_last_use; update last use\n",
    "        longest_time = 0.0\n",
    "        for qubit in hashable_gate.qubits:\n",
    "            if qubit in qubit_last_use:\n",
    "                if qubit_last_use[qubit][1]>= longest_time:\n",
    "                    longest_time = qubit_last_use[qubit][1]\n",
    "        for qubit in hashable_gate.qubits:\n",
    "            diff=0.0\n",
    "            if qubit in qubit_last_use:\n",
    "                diff = longest_time-qubit_last_use[qubit][1]\n",
    "                g.add_edge(qubit_last_use[qubit][0], hashable_gate,weight=timestamp+longest_time-qubit_last_use[qubit][1])\n",
    "            else:\n",
    "                g.add_edge(-1, hashable_gate,weight=timestamp+longest_time)\n",
    "            \n",
    "            qubit_last_use[qubit] =  [hashable_gate, qubit_last_use.get(qubit, [None, 0])[1] + timestamp+diff]\n",
    "            \n",
    "    # Add the end node\n",
    "    g.add_node(float('inf'))\n",
    "    \n",
    "    for qubit in qubit_last_use:\n",
    "        g.add_edge(qubit_last_use[qubit][0], float('inf'))\n",
    "            \n",
    "    return g\n",
    "\n",
    "def schedule_circuit(routed_circuit:QuantumCircuit, gate_times:Dict[str, float],\n",
    "                     T1_times:Dict[int, float],\n",
    "                     xtalk_errors:Dict[Tuple[Tuple[int, int], Tuple[int, int]], float]\n",
    "                    ) -> Dict[GateWrapper, float]:\n",
    "    '''\n",
    "    This function should take an input program given as a qiskit.QuantumCircuit, \n",
    "    This circuit should be routed already!\n",
    "\n",
    "    You should return the start time for every gate in the circuit as a\n",
    "    dictionary: {GateWrapper(...) : start_time}, where GateWrapper is our above\n",
    "    implementation which wraps qiskit Gates.\n",
    "\n",
    "    Hints:\n",
    "        - It may be helpful to build a dependency graph weighted using these gate\n",
    "        times and use nx.all_pairs_dijkstra_path_length.\n",
    "        - Create the schedule keys out of qiskit gates with:\n",
    "            GateWrapper(gate[0], gate[1], gate[2], label=i)  # Unique values of i\n",
    "    '''\n",
    "    ### YOUR SOLUTION IN THIS FUNCTION\n",
    "    ### You may use your solution from the previous lab as a starting point\n",
    "    \n",
    "    # Convert SWAP gates to 3 CNOT gates\n",
    "    pm = qiskit.transpiler.PassManager([qiskit.transpiler.passes.Unroller(['rz', 'rx', 'h', 'u3', 'cx'])])\n",
    "    routed_circuit = pm.run(routed_circuit)\n",
    "\n",
    "    # May be useful but not required\n",
    "    dep_graph = build_weighted_program_dependency_graph(routed_circuit,gate_times)\n",
    "    #time_length = dict(nx.all_pairs_dijkstra_path_length(dep_graph))\n",
    "\n",
    "    # A dictionary of gate->start_time\n",
    "    gate_start_times:Dict[GateWrapper, float] = {}\n",
    "\n",
    "    # YOUR CODE HERE\n",
    "    timestamp=0.0\n",
    "    \n",
    "    # Compute the shortest path lengths from node '-1' to all other nodes\n",
    "    path_lengths_from_start = dict(nx.single_source_dijkstra_path_length(dep_graph, -1))\n",
    "\n",
    "    for gate in nx.topological_sort(dep_graph):\n",
    "        if gate not in [-1, float('inf')]:\n",
    "            timestamp = path_lengths_from_start[gate]\n",
    "            if isinstance(gate.gate, qiskit.circuit.library.standard_gates.CXGate):\n",
    "                timestamp-=gate_times['cx']\n",
    "            else:\n",
    "                if isinstance(gate.gate, qiskit.circuit.library.standard_gates.rx.RXGate) or \\\n",
    "                isinstance(gate.gate, qiskit.circuit.library.standard_gates.h.HGate) or \\\n",
    "                isinstance(gate.gate, qiskit.circuit.library.standard_gates.u3.U3Gate):\n",
    "                    timestamp -= gate_times['1']\n",
    "                else: timestamp-=gate_times['rz']\n",
    "\n",
    "            gate_start_times[gate]=timestamp\n",
    "    \n",
    "\n",
    "    return gate_start_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3593926637.py:14: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  args = ','.join(str(q.index) for q in self.qubits)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{0{cx(0,1)}: 0.0,\n",
       " 1{cx(2,3)}: 0.0,\n",
       " 116{cx(6,5)}: 0.0,\n",
       " 2{cx(0,4)}: 100.0,\n",
       " 3{h(0)}: 200.0,\n",
       " 4{cx(1,0)}: 210.0,\n",
       " 5{rz(0)}: 310.0,\n",
       " 6{cx(4,0)}: 310.0,\n",
       " 7{rz(0)}: 410.0,\n",
       " 8{cx(1,0)}: 410.0,\n",
       " 9{rz(0)}: 510.0,\n",
       " 10{rz(1)}: 510.0,\n",
       " 11{cx(4,0)}: 510.0,\n",
       " 12{rz(0)}: 610.0,\n",
       " 13{rx(0)}: 610.0,\n",
       " 14{rz(0)}: 620.0,\n",
       " 15{cx(0,4)}: 620.0,\n",
       " 16{cx(4,0)}: 720.0,\n",
       " 17{cx(0,4)}: 820.0,\n",
       " 18{cx(0,1)}: 920.0,\n",
       " '...': '...'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You may modify this cell to test scores for your mapper on various circuits\n",
    "\n",
    "#mapping = map_circuit(cuccaro, grid_hardware)\n",
    "#routed = route_circuit(cuccaro, grid_hardware, mapping)\n",
    "\n",
    "# To test your implementation against other circuits, copy your\n",
    "# mapping and routing solutions from the previous lab,\n",
    "# uncomment the above lines, and remove the below line\n",
    "routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "\n",
    "schedule = schedule_circuit(routed, sample_gate_times, sample_T1_times, sample_xtalk_errors)\n",
    "dict(list(schedule.items())[:20]+[('...', '...')])  # Only print first 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/741747216.py:7: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  circuit = QuantumCircuit(next(iter(qubits)).register)\n",
      "/tmp/ipykernel_60/741747216.py:12: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 19183.6x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do not modify this function\n",
    "def schedule_to_circuit(schedule):\n",
    "    qubits = set()\n",
    "    for gate in schedule.keys():\n",
    "        qubits.update(gate.qubits)\n",
    "    n = len(qubits)\n",
    "    circuit = QuantumCircuit(next(iter(qubits)).register)\n",
    "    ordered = sorted(((time, gate)\n",
    "                      for gate, time in schedule.items()),\n",
    "                     key=lambda x:(x[0], x[1].gate.name!='rz'))\n",
    "    for time, gate in ordered:\n",
    "        new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n",
    "        circuit.append(gate.gate, new_qubits, [])\n",
    "    return circuit\n",
    "\n",
    "# Make sure the schedule still represents the original circuit\n",
    "schedule_to_circuit(schedule).draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e34fa286df8274327a6770633c08b9f4",
     "grade": true,
     "grade_id": "1-helpers",
     "locked": true,
     "points": 0,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "def validate_schedule(routed_circuit, gate_times, schedule, check_unitary=True):\n",
    "    '''Set check_unitary=False for circuits with over 10 qubits.'''\n",
    "    # Check other arguments\n",
    "    assert isinstance(routed_circuit, QuantumCircuit)\n",
    "    assert isinstance(gate_times, dict)\n",
    "    assert isinstance(schedule, dict)\n",
    "    assert (len(routed_circuit.qubits)\n",
    "            == len(frozenset(q.index for q in routed_circuit.qubits))), (\n",
    "        'Unsupported: circuits with multiple registers')\n",
    "    # Convert schedule back to a circuit\n",
    "    s_circuit = schedule_to_circuit(schedule)\n",
    "    r_qubits = frozenset(q for q in routed_circuit.qubits)\n",
    "    s_qubits = frozenset(q for q in routed_circuit.qubits)\n",
    "    assert r_qubits == s_qubits, 'schedule has different qubits than the circuit'\n",
    "    # Check allowed gates\n",
    "    for gate in schedule.keys():\n",
    "        if len(gate.qubits) < 2: continue\n",
    "        assert len(gate.qubits) <= 2, (\n",
    "            'Only one- and two-qubit gates are allowed in the schedule')\n",
    "        assert isinstance(gate.gate, qiskit.circuit.library.standard_gates.x.CXGate), (\n",
    "            'Two-qubit gates other than CNOT are not allwed in the schedule')\n",
    "    # Check for overlapping gates\n",
    "    sorted_schedule = sorted(schedule.items(), key=lambda x:x[1])\n",
    "    for i, (gate, time) in enumerate(sorted_schedule):\n",
    "        end_time = time + gate_times.get(gate.gate.name, gate_times['1'])\n",
    "        j = 0\n",
    "        for j in range(i+1, len(sorted_schedule)):\n",
    "            if sorted_schedule[j][1] >= end_time: break\n",
    "        nearby_gates = sorted_schedule[i+1:j]\n",
    "        left = max((\n",
    "            t2 + gate_times.get(g2.gate.name, gate_times['1'])\n",
    "            for g2, t2 in nearby_gates\n",
    "            if (t2 + gate_times.get(g2.gate.name, gate_times['1']) < end_time\n",
    "                and len(set(gate.qubits).intersection(g2.qubits)) > 0)\n",
    "                and g2 != gate),\n",
    "            default=0\n",
    "        )\n",
    "        right = min((\n",
    "            t2\n",
    "            for g2, t2 in nearby_gates\n",
    "            if ((t2 > time or (\n",
    "                    gate_times.get(g2.gate.name, gate_times['1']) != 0\n",
    "                    and t2 >= time))\n",
    "                and len(set(gate.qubits).intersection(g2.qubits)) > 0)\n",
    "                and g2 != gate),\n",
    "            default=float('inf')\n",
    "        )\n",
    "        #print(left, right)\n",
    "        assert left <= time+1e-8 and right >= end_time-1e-8, (\n",
    "            'At least one gate is scheduled during the same time on the same qubits '\n",
    "            f'(at time {time} - {end_time}, gate={gate})')\n",
    "        \n",
    "    # Check unitary\n",
    "    if check_unitary:  # Slow for circuits with more than 10 qubits\n",
    "        from qiskit.quantum_info.operators.predicates import matrix_equal\n",
    "        unitary = lambda c: qiskit.quantum_info.Operator(c).data\n",
    "        assert matrix_equal(unitary(s_circuit), unitary(routed_circuit)), (\n",
    "            'schedule is not equivalent to the input circuit')\n",
    "\n",
    "def total_schedule_active_times(routed_circuit, gate_times, schedule):\n",
    "    total_duration = max((\n",
    "        time + gate_times.get(gate.gate.name, gate_times['1'])\n",
    "        for gate, time in schedule.items()),\n",
    "        default=0\n",
    "    )\n",
    "    first_use_each_qubit = {\n",
    "        q: min((time for gate, time in schedule.items()\n",
    "                     if q in gate.qubits),\n",
    "               default=total_duration)\n",
    "        for q in routed_circuit.qubits\n",
    "    }\n",
    "    qubit_usage_times = {\n",
    "        q: total_duration - first_use\n",
    "        for q, first_use in first_use_each_qubit.items()\n",
    "    }\n",
    "    total_active = sum(qubit_usage_times.values())\n",
    "    \n",
    "    num_used_qubits = sum(t > 0 for t in qubit_usage_times.values())\n",
    "    worst_serial_time = sum(\n",
    "        gate_times.get(gate.gate.name, gate_times['1'])\n",
    "        for gate in schedule.keys()\n",
    "    )\n",
    "    #print(f'(Total circuit duration is {total_duration})')\n",
    "    #print(f'Qubit active durations:\\n{qubit_usage_times}')\n",
    "    return qubit_usage_times, worst_serial_time\n",
    "\n",
    "def total_schedule_cross_talk(routed_circuit, gate_times, schedule, xtalk_errors):\n",
    "    cx_dur = gate_times['cx']\n",
    "    cx_times_list = [\n",
    "        (time, time+cx_dur, frozenset(q.index for q in gate.qubits))\n",
    "        for gate, time in schedule.items()\n",
    "        if gate.gate.name == 'cx'  # Only count crosstalk for CNOT gates\n",
    "    ]\n",
    "    cx_times_list.sort()\n",
    "\n",
    "    xtalk_list = []\n",
    "    for i, (start1, end1, qubits1) in enumerate(cx_times_list):\n",
    "        for j in range(i+1, len(cx_times_list)):\n",
    "            start2, end2, qubits2 = cx_times_list[j]\n",
    "            if start2 < end1-1e-8:  # Overlap in time -> crosstalk\n",
    "                key = tuple(sorted([tuple(sorted(qubits1)), tuple(sorted(qubits2))]))\n",
    "                xtalk_list.append(xtalk_errors.get(key, 1))\n",
    "            else:\n",
    "                break\n",
    "    return xtalk_list\n",
    "\n",
    "def score_schedule(routed_circuit, gate_times, schedule, t1_times, xtalk_errors):\n",
    "    '''\n",
    "    Computes the active qubit time of the schedule.\n",
    "    \n",
    "    Returns the tuple (your score, worst-case serial score).  Lower values are better.\n",
    "    '''\n",
    "    validate_schedule(routed_circuit, gate_times, schedule, check_unitary=False)\n",
    "\n",
    "    # Coherence time error estimate\n",
    "    qubit_usage_times, worst_serial_time = total_schedule_active_times(\n",
    "            routed_circuit, gate_times, schedule)\n",
    "    total_duration = max(qubit_usage_times.values())\n",
    "    total_active = sum(qubit_usage_times.values())\n",
    "    each_coherence = {\n",
    "        q.index: np.exp(-t/t1_times[q.index])\n",
    "        for q, t in qubit_usage_times.items()\n",
    "    }\n",
    "    coherence_success = np.prod(list(each_coherence.values()))\n",
    "\n",
    "    # Crosstalk error estimate\n",
    "    xtalk_list = total_schedule_cross_talk(\n",
    "            routed_circuit, gate_times, schedule, xtalk_errors)\n",
    "    xtalk_success = np.prod(xtalk_list)\n",
    "    \n",
    "    # Total\n",
    "    score = coherence_success * xtalk_success\n",
    "    \n",
    "    # Compute worst serialized schedule score\n",
    "    serial_each_coherence = {\n",
    "        q.index: np.exp(-worst_serial_time/t1_times[q.index])\n",
    "        for q, _ in qubit_usage_times.items()\n",
    "    }\n",
    "    serial_coherence_success = np.prod(list(serial_each_coherence.values()))\n",
    "    serial_xtalk_success = 1.0\n",
    "    worst_serial_score = serial_coherence_success * serial_xtalk_success\n",
    "    \n",
    "    print(f'(Total circuit duration is {total_duration})')\n",
    "    print(f'({len(t1_times)} qubit coherence times ranging from {min(t1_times.values())} to {max(t1_times.values())})')\n",
    "    #print(f'Crosstalk list: {xtalk_list}')\n",
    "    #print(f'Usage times: {t1_times}')\n",
    "    #print(f'Each coherence: {each_coherence}')\n",
    "    print(f'Coherence success rate: {coherence_success} (P(error)={1-coherence_success})')\n",
    "    print(f'Crosstalk success rate: {xtalk_success} (P(error)={1-xtalk_success})')\n",
    "    print(f'Overall success rate: {score}')\n",
    "    print()\n",
    "    print(f'Your scheduling score is P(success)={score}')\n",
    "    print(f'(worst case serialized score would be {worst_serial_score})')\n",
    "    return score, worst_serial_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Total circuit duration is 19140.0)\n",
      "(8 qubit coherence times ranging from 50005.71874086725 to 86016.2246721079)\n",
      "Coherence success rate: 0.09723524199538756 (P(error)=0.9027647580046124)\n",
      "Crosstalk success rate: 0.22415822736979457 (P(error)=0.7758417726302054)\n",
      "Overall success rate: 0.021796079483559085\n",
      "\n",
      "Your scheduling score is P(success)=0.021796079483559085\n",
      "(worst case serialized score would be 0.06738362588013197)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:9: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  == len(frozenset(q.index for q in routed_circuit.qubits))), (\n",
      "/tmp/ipykernel_60/741747216.py:7: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  circuit = QuantumCircuit(next(iter(qubits)).register)\n",
      "/tmp/ipykernel_60/741747216.py:12: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n",
      "/tmp/ipykernel_60/3584222154.py:122: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-t/t1_times[q.index])\n",
      "/tmp/ipykernel_60/3584222154.py:91: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  (time, time+cx_dur, frozenset(q.index for q in gate.qubits))\n",
      "/tmp/ipykernel_60/3584222154.py:137: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-worst_serial_time/t1_times[q.index])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.021796079483559085, 0.06738362588013197)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You may modify this cell to test scores for your mapper on various circuits\n",
    "\n",
    "#mapping = map_circuit(cuccaro, grid_hardware)\n",
    "#routed = route_circuit(cuccaro, grid_hardware, mapping)\n",
    "\n",
    "# To test your implementation against other circuits, copy your\n",
    "# mapping and routing solutions from the previous lab,\n",
    "# uncomment the above lines, and remove the below line\n",
    "routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "\n",
    "schedule = schedule_circuit(routed, sample_gate_times, sample_T1_times, sample_xtalk_errors)\n",
    "score_schedule(routed, sample_gate_times, schedule, sample_T1_times, sample_xtalk_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "590b5e2404a5908a1e03b7123556eb09",
     "grade": true,
     "grade_id": "1a",
     "locked": true,
     "points": 30,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:9: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  == len(frozenset(q.index for q in routed_circuit.qubits))), (\n",
      "/tmp/ipykernel_60/741747216.py:7: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  circuit = QuantumCircuit(next(iter(qubits)).register)\n",
      "/tmp/ipykernel_60/741747216.py:12: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Total circuit duration is 15328.0)\n",
      "(8 qubit coherence times ranging from 46341 to 126789)\n",
      "Coherence success rate: 0.28845380034042273 (P(error)=0.7115461996595773)\n",
      "Crosstalk success rate: 0.06904687030731041 (P(error)=0.9309531296926896)\n",
      "Overall success rate: 0.01991683214175598\n",
      "\n",
      "Your scheduling score is P(success)=0.01991683214175598\n",
      "(worst case serialized score would be 0.21604888027264835)\n",
      "\n",
      "PASS: Valid circuit schedule\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:122: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-t/t1_times[q.index])\n",
      "/tmp/ipykernel_60/3584222154.py:91: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  (time, time+cx_dur, frozenset(q.index for q in gate.qubits))\n",
      "/tmp/ipykernel_60/3584222154.py:137: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-worst_serial_time/t1_times[q.index])\n"
     ]
    }
   ],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the function schedule_circuit.\n",
    "# This test is worth more points than the other parts\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {\n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "print('\\nPASS: Valid circuit schedule')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "c3346342d27195630d7cf68c69a72401",
     "grade": true,
     "grade_id": "1b",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:9: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  == len(frozenset(q.index for q in routed_circuit.qubits))), (\n",
      "/tmp/ipykernel_60/741747216.py:7: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  circuit = QuantumCircuit(next(iter(qubits)).register)\n",
      "/tmp/ipykernel_60/741747216.py:12: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Total circuit duration is 15328.0)\n",
      "(8 qubit coherence times ranging from 46341 to 126789)\n",
      "Coherence success rate: 0.28845380034042273 (P(error)=0.7115461996595773)\n",
      "Crosstalk success rate: 1 (P(error)=0)\n",
      "Overall success rate: 0.28845380034042273\n",
      "\n",
      "Your scheduling score is P(success)=0.28845380034042273\n",
      "(worst case serialized score would be 0.21604888027264835)\n",
      "\n",
      "PASS: Score is above P=0.25 (see if you can beat our score of 0.3211)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:122: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-t/t1_times[q.index])\n",
      "/tmp/ipykernel_60/3584222154.py:91: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  (time, time+cx_dur, frozenset(q.index for q in gate.qubits))\n",
      "/tmp/ipykernel_60/3584222154.py:137: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-worst_serial_time/t1_times[q.index])\n"
     ]
    }
   ],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce T1 decoherence error.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  # No crosstalk errors in this test\n",
    "    tuple(sorted([e1, e2])): 1 #np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.21604888 <= _worst <= 0.21604889, 'internal error'\n",
    "assert _score >= 0.25, 'score is below P=0.25, try improving the schedule by scheduling gates sooner if possible'\n",
    "print('\\nPASS: Score is above P=0.25 (see if you can beat our score of 0.3211)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "0954c7c068db0949c651da588d82e88d",
     "grade": true,
     "grade_id": "1c",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:9: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  == len(frozenset(q.index for q in routed_circuit.qubits))), (\n",
      "/tmp/ipykernel_60/741747216.py:7: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.register`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  circuit = QuantumCircuit(next(iter(qubits)).register)\n",
      "/tmp/ipykernel_60/741747216.py:12: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  new_qubits = [circuit.qubits[q.index] for q in gate.qubits]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Total circuit duration is 15328.0)\n",
      "(8 qubit coherence times ranging from 46341 to 126789)\n",
      "Coherence success rate: 0.28845380034042273 (P(error)=0.7115461996595773)\n",
      "Crosstalk success rate: 1 (P(error)=0)\n",
      "Overall success rate: 0.28845380034042273\n",
      "\n",
      "Your scheduling score is P(success)=0.28845380034042273\n",
      "(worst case serialized score would be 0.21604888027264835)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_60/3584222154.py:122: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-t/t1_times[q.index])\n",
      "/tmp/ipykernel_60/3584222154.py:91: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  (time, time+cx_dur, frozenset(q.index for q in gate.qubits))\n",
      "/tmp/ipykernel_60/3584222154.py:137: DeprecationWarning: The property ``qiskit.circuit.bit.Bit.index`` is deprecated as of qiskit-terra 0.17. It will be removed no earlier than 3 months after the release date. Instead, use :meth:`~qiskit.circuit.quantumcircuit.QuantumCircuit.find_bit` to find all the containing registers within a circuit and the index of the bit within the circuit.\n",
      "  q.index: np.exp(-worst_serial_time/t1_times[q.index])\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "score is below P=0.30, try improving the schedule by scheduling gates sooner if possible",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m _score, _worst \u001b[38;5;241m=\u001b[39m score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;241m0.21604888\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m _worst \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.21604889\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minternal error\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m _score \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.30\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscore is below P=0.30, try improving the schedule by scheduling gates sooner if possible\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mPASS: Score is above P=0.30 (see if you can beat our score of 0.3211)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mAssertionError\u001b[0m: score is below P=0.30, try improving the schedule by scheduling gates sooner if possible"
     ]
    }
   ],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce T1 decoherence error again.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  # No crosstalk errors in this test\n",
    "    tuple(sorted([e1, e2])): 1 #np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.21604888 <= _worst <= 0.21604889, 'internal error'\n",
    "assert _score >= 0.30, 'score is below P=0.30, try improving the schedule by scheduling gates sooner if possible'\n",
    "print('\\nPASS: Score is above P=0.30 (see if you can beat our score of 0.3211)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "1ffa08c48966becc4eb4a2c96c0498df",
     "grade": true,
     "grade_id": "1d",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce crosstalk error.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {  # No decoherence errors in this test\n",
    "    i: np.inf * np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  \n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "# The worst-case serialized score is a perfect 1.0 because it already avoids all crosstalk\n",
    "assert _worst == 1.0, 'internal error'\n",
    "assert _score >= 0.10, 'score is below P=0.10, try improving the schedule by not scheduling CNOT gates at the same time'\n",
    "print('\\nPASS: Score is above P=0.10 (see if you can beat our score of 1.0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "f0238375665136bdfe08008e6b0d647b",
     "grade": true,
     "grade_id": "1e",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce crosstalk error again.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {  # No decoherence errors in this test\n",
    "    i: np.inf * np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  \n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "# The worst-case serialized score is a perfect 1.0 because it already avoids all crosstalk\n",
    "assert _worst == 1.0, 'internal error'\n",
    "assert _score >= 0.50, 'score is below P=0.50, try improving the schedule by not scheduling CNOT gates at the same time'\n",
    "print('\\nPASS: Score is above P=0.50 (see if you can beat our score of 1.0)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "f6cdadb3de40da1830092c0f5553d61a",
     "grade": true,
     "grade_id": "1f",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce both kinds of error at once.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  \n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.21604888 <= _worst <= 0.21604889, 'internal error'\n",
    "assert _score >= 0.22, 'score is below P=0.22, try improving the schedule'\n",
    "print('\\nPASS: Score is above P=0.22 (see if you can beat our score of 0.3211)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "4b4d125f2d810a18d1f2da65e060c3a6",
     "grade": true,
     "grade_id": "1g",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the how well you reduce both kinds of error at once.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(2, 4)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[0],q[1];\\ncx q[0],q[4];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\ncx q[6],q[5];\\ncx q[6],q[2];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[7];\\nh q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[6];\\ncx q[5],q[6];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(-5*pi/4) q[6];\\nrx(pi/2) q[6];\\nrz(pi/2) q[6];\\nrz(pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[5];\\nswap q[1],q[2];\\ncx q[1],q[5];\\nswap q[1],q[2];\\ncx q[6],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[4],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[1];\\nswap q[0],q[4];\\ncx q[0],q[1];\\nswap q[0],q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\ncx q[2],q[3];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[6],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(724940)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(4e4, 14e4)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {  \n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.85, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.21604888 <= _worst <= 0.21604889, 'internal error'\n",
    "assert _score >= 0.28, 'score is below P=0.28, try improving the schedule'\n",
    "print('\\nPASS: Score is above P=0.28 (see if you can beat our score of 0.3211)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "a74f93a94f4774cc5689583f8141688f",
     "grade": true,
     "grade_id": "1h",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test with bigger circuits now.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(6, 6)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[36];\\ncx q[0],q[1];\\ncx q[0],q[6];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(pi/4) q[6];\\nrz(-pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(pi/4) q[3];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\ncx q[8],q[7];\\ncx q[8],q[2];\\nh q[8];\\ncx q[7],q[8];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nrz(pi/4) q[8];\\ncx q[7],q[8];\\nrz(pi/4) q[7];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(2.6375741) q[7];\\nrx(pi) q[7];\\nrz(2.6375741) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(-5*pi/4) q[8];\\nrx(pi/2) q[8];\\nrz(pi/2) q[8];\\ncx q[9],q[10];\\ncx q[9],q[8];\\nh q[9];\\ncx q[10],q[9];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nrz(pi/4) q[9];\\ncx q[10],q[9];\\nrz(pi/4) q[10];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-pi/4) q[10];\\nrz(pi/4) q[8];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(2.6375741) q[10];\\nrx(pi) q[10];\\nrz(2.6375741) q[10];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-5*pi/4) q[9];\\nrx(pi/2) q[9];\\nrz(pi/2) q[9];\\ncx q[15],q[14];\\ncx q[15],q[9];\\nh q[15];\\ncx q[14],q[15];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nrz(pi/4) q[15];\\ncx q[14],q[15];\\nrz(pi/4) q[14];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-pi/4) q[14];\\nrz(pi/4) q[9];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(2.6375741) q[14];\\nrx(pi) q[14];\\nrz(2.6375741) q[14];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-5*pi/4) q[15];\\nrx(pi/2) q[15];\\nrz(pi/2) q[15];\\ncx q[16],q[17];\\ncx q[16],q[15];\\nh q[16];\\ncx q[17],q[16];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nrz(pi/4) q[16];\\ncx q[17],q[16];\\nrz(pi/4) q[17];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-pi/4) q[17];\\nrz(pi/4) q[15];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(2.6375741) q[17];\\nrx(pi) q[17];\\nrz(2.6375741) q[17];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-5*pi/4) q[16];\\nrx(pi/2) q[16];\\nrz(pi/2) q[16];\\ncx q[22],q[21];\\ncx q[22],q[16];\\nh q[22];\\ncx q[21],q[22];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nrz(pi/4) q[22];\\ncx q[21],q[22];\\nrz(pi/4) q[21];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-pi/4) q[21];\\nrz(pi/4) q[16];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(2.6375741) q[21];\\nrx(pi) q[21];\\nrz(2.6375741) q[21];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-5*pi/4) q[22];\\nrx(pi/2) q[22];\\nrz(pi/2) q[22];\\ncx q[23],q[29];\\ncx q[23],q[22];\\nh q[23];\\ncx q[29],q[23];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nrz(pi/4) q[23];\\ncx q[29],q[23];\\nrz(pi/4) q[29];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-pi/4) q[29];\\nrz(pi/4) q[22];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(2.6375741) q[29];\\nrx(pi) q[29];\\nrz(2.6375741) q[29];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-5*pi/4) q[23];\\nrx(pi/2) q[23];\\nrz(pi/2) q[23];\\ncx q[11],q[5];\\nswap q[17],q[11];\\ncx q[17],q[23];\\nswap q[17],q[11];\\nh q[11];\\ncx q[5],q[11];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nrz(pi/4) q[11];\\ncx q[5],q[11];\\nrz(pi/4) q[5];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-pi/4) q[5];\\nrz(pi/4) q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-5*pi/4) q[11];\\nrx(pi/2) q[11];\\nrz(pi/2) q[11];\\nswap q[10],q[4];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[4];\\nswap q[10],q[4];\\ncx q[10],q[11];\\nswap q[10],q[4];\\nh q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(pi/4) q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(pi/4) q[28];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(-5*pi/4) q[4];\\nrx(pi/2) q[4];\\nrz(pi/2) q[4];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(-pi/4) q[28];\\nrz(pi/4) q[11];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(2.6375741) q[28];\\nrx(pi) q[28];\\nrz(2.6375741) q[28];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\ncx q[13],q[12];\\nswap q[7],q[13];\\nswap q[1],q[7];\\nswap q[2],q[1];\\nswap q[3],q[2];\\ncx q[3],q[4];\\nswap q[3],q[2];\\nswap q[2],q[1];\\nswap q[1],q[7];\\nswap q[7],q[13];\\nh q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(-5*pi/4) q[13];\\nrx(pi/2) q[13];\\nrz(pi/2) q[13];\\nrz(pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(2.6375741) q[12];\\nrx(pi) q[12];\\nrz(2.6375741) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\ncx q[19],q[18];\\ncx q[19],q[13];\\nh q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(pi/4) q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(-5*pi/4) q[19];\\nrx(pi/2) q[19];\\nrz(pi/2) q[19];\\nrz(pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(pi/4) q[13];\\nrz(-pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(2.6375741) q[18];\\nrx(pi) q[18];\\nrz(2.6375741) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\ncx q[20],q[26];\\ncx q[20],q[19];\\nh q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(pi/4) q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(-5*pi/4) q[20];\\nrx(pi/2) q[20];\\nrz(pi/2) q[20];\\nrz(pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(pi/4) q[19];\\nrz(-pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(2.6375741) q[26];\\nrx(pi) q[26];\\nrz(2.6375741) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\ncx q[25],q[24];\\nswap q[19],q[25];\\ncx q[19],q[20];\\nswap q[19],q[25];\\nh q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(pi/4) q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(-5*pi/4) q[25];\\nrx(pi/2) q[25];\\nrz(pi/2) q[25];\\nrz(pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(pi/4) q[20];\\nrz(-pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(2.6375741) q[24];\\nrx(pi) q[24];\\nrz(2.6375741) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\ncx q[31],q[30];\\ncx q[31],q[25];\\nh q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(pi/4) q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(-5*pi/4) q[31];\\nrx(pi/2) q[31];\\nrz(pi/2) q[31];\\nrz(pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(pi/4) q[25];\\nrz(-pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(2.6375741) q[30];\\nrx(pi) q[30];\\nrz(2.6375741) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\ncx q[32],q[33];\\ncx q[32],q[31];\\nh q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(pi/4) q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(-5*pi/4) q[32];\\nrx(pi/2) q[32];\\nrz(pi/2) q[32];\\nrz(pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(pi/4) q[31];\\nrz(-pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(2.6375741) q[33];\\nrx(pi) q[33];\\nrz(2.6375741) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nswap q[33],q[27];\\ncx q[33],q[34];\\nswap q[33],q[27];\\nswap q[26],q[27];\\ncx q[26],q[32];\\nswap q[26],q[27];\\nh q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(pi/4) q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(-5*pi/4) q[27];\\nrx(pi/2) q[27];\\nrz(pi/2) q[27];\\nrz(pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(pi/4) q[32];\\nrz(-pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(2.6375741) q[34];\\nrx(pi) q[34];\\nrz(2.6375741) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nswap q[33],q[27];\\nswap q[34],q[33];\\ncx q[34],q[35];\\nswap q[34],q[33];\\nswap q[33],q[27];\\nh q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(pi/4) q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(-5*pi/4) q[27];\\nrx(pi/2) q[27];\\nrz(pi/2) q[27];\\nrz(pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(pi/4) q[32];\\nrz(-pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nswap q[26],q[27];\\ncx q[26],q[32];\\nswap q[26],q[27];\\nh q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(pi/4) q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(-5*pi/4) q[32];\\nrx(pi/2) q[32];\\nrz(pi/2) q[32];\\nrz(pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(pi/4) q[31];\\nrz(-pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\ncx q[32],q[31];\\nh q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(pi/4) q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(-5*pi/4) q[31];\\nrx(pi/2) q[31];\\nrz(pi/2) q[31];\\nrz(pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(pi/4) q[25];\\nrz(-pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\ncx q[31],q[25];\\nh q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(pi/4) q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(-5*pi/4) q[25];\\nrx(pi/2) q[25];\\nrz(pi/2) q[25];\\nrz(pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(pi/4) q[20];\\nrz(-pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nswap q[19],q[25];\\ncx q[19],q[20];\\nswap q[19],q[25];\\nh q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(pi/4) q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(-5*pi/4) q[20];\\nrx(pi/2) q[20];\\nrz(pi/2) q[20];\\nrz(pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(pi/4) q[19];\\nrz(-pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\ncx q[20],q[19];\\nh q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(pi/4) q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(-5*pi/4) q[19];\\nrx(pi/2) q[19];\\nrz(pi/2) q[19];\\nrz(pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(pi/4) q[13];\\nrz(-pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\ncx q[19],q[13];\\nh q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(-5*pi/4) q[13];\\nrx(pi/2) q[13];\\nrz(pi/2) q[13];\\nrz(pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nswap q[7],q[13];\\nswap q[1],q[7];\\nswap q[2],q[1];\\nswap q[3],q[2];\\ncx q[3],q[4];\\nswap q[3],q[2];\\nswap q[2],q[1];\\nswap q[1],q[7];\\nswap q[7],q[13];\\nh q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(2.6375741) q[12];\\nrx(pi) q[12];\\nrz(2.6375741) q[12];\\ncx q[13],q[12];\\nrz(2.6375741) q[18];\\nrx(pi) q[18];\\nrz(2.6375741) q[18];\\ncx q[19],q[18];\\nrz(2.6375741) q[26];\\nrx(pi) q[26];\\nrz(2.6375741) q[26];\\ncx q[20],q[26];\\nrz(2.6375741) q[24];\\nrx(pi) q[24];\\nrz(2.6375741) q[24];\\ncx q[25],q[24];\\nrz(2.6375741) q[30];\\nrx(pi) q[30];\\nrz(2.6375741) q[30];\\ncx q[31],q[30];\\nrz(2.6375741) q[33];\\nrx(pi) q[33];\\nrz(2.6375741) q[33];\\ncx q[32],q[33];\\nrz(2.6375741) q[34];\\nrx(pi) q[34];\\nrz(2.6375741) q[34];\\nswap q[33],q[27];\\ncx q[33],q[34];\\nswap q[33],q[27];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(pi/4) q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(pi/4) q[28];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(-5*pi/4) q[4];\\nrx(pi/2) q[4];\\nrz(pi/2) q[4];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(-pi/4) q[28];\\nrz(pi/4) q[11];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nswap q[10],q[4];\\ncx q[10],q[11];\\nswap q[10],q[4];\\nrz(2.6375741) q[28];\\nrx(pi) q[28];\\nrz(2.6375741) q[28];\\nswap q[10],q[4];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[4];\\nh q[11];\\ncx q[5],q[11];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nrz(pi/4) q[11];\\ncx q[5],q[11];\\nrz(pi/4) q[5];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-pi/4) q[5];\\nrz(pi/4) q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nrz(-5*pi/4) q[11];\\nrx(pi/2) q[11];\\nrz(pi/2) q[11];\\nswap q[17],q[11];\\ncx q[17],q[23];\\nswap q[17],q[11];\\nh q[23];\\ncx q[29],q[23];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nrz(pi/4) q[23];\\ncx q[29],q[23];\\nrz(pi/4) q[29];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-pi/4) q[29];\\nrz(pi/4) q[22];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(2.6375741) q[29];\\nrx(pi) q[29];\\nrz(2.6375741) q[29];\\nrz(-5*pi/4) q[23];\\nrx(pi/2) q[23];\\nrz(pi/2) q[23];\\ncx q[23],q[22];\\nh q[22];\\ncx q[21],q[22];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nrz(pi/4) q[22];\\ncx q[21],q[22];\\nrz(pi/4) q[21];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-pi/4) q[21];\\nrz(pi/4) q[16];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(2.6375741) q[21];\\nrx(pi) q[21];\\nrz(2.6375741) q[21];\\nrz(-5*pi/4) q[22];\\nrx(pi/2) q[22];\\nrz(pi/2) q[22];\\ncx q[22],q[16];\\nh q[16];\\ncx q[17],q[16];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nrz(pi/4) q[16];\\ncx q[17],q[16];\\nrz(pi/4) q[17];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-pi/4) q[17];\\nrz(pi/4) q[15];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(2.6375741) q[17];\\nrx(pi) q[17];\\nrz(2.6375741) q[17];\\nrz(-5*pi/4) q[16];\\nrx(pi/2) q[16];\\nrz(pi/2) q[16];\\ncx q[16],q[15];\\nh q[15];\\ncx q[14],q[15];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nrz(pi/4) q[15];\\ncx q[14],q[15];\\nrz(pi/4) q[14];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-pi/4) q[14];\\nrz(pi/4) q[9];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(2.6375741) q[14];\\nrx(pi) q[14];\\nrz(2.6375741) q[14];\\nrz(-5*pi/4) q[15];\\nrx(pi/2) q[15];\\nrz(pi/2) q[15];\\ncx q[15],q[9];\\nh q[9];\\ncx q[10],q[9];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nrz(pi/4) q[9];\\ncx q[10],q[9];\\nrz(pi/4) q[10];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-pi/4) q[10];\\nrz(pi/4) q[8];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(2.6375741) q[10];\\nrx(pi) q[10];\\nrz(2.6375741) q[10];\\nrz(-5*pi/4) q[9];\\nrx(pi/2) q[9];\\nrz(pi/2) q[9];\\ncx q[9],q[8];\\nh q[8];\\ncx q[7],q[8];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nrz(pi/4) q[8];\\ncx q[7],q[8];\\nrz(pi/4) q[7];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(2.6375741) q[7];\\nrx(pi) q[7];\\nrz(2.6375741) q[7];\\nrz(-5*pi/4) q[8];\\nrx(pi/2) q[8];\\nrz(pi/2) q[8];\\ncx q[8],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(pi/4) q[3];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(pi/4) q[6];\\nrz(-pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\ncx q[0],q[6];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\ncx q[2],q[3];\\ncx q[8],q[7];\\ncx q[9],q[10];\\ncx q[15],q[14];\\ncx q[16],q[17];\\ncx q[22],q[21];\\ncx q[23],q[29];\\ncx q[11],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(408458)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(1e6, 3e6)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {\n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.985, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule, check_unitary=False)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.08678814 <= _worst <= 0.08678815, 'internal error'\n",
    "assert _score >= 0.05, 'score is below P=0.05, try improving the schedule'\n",
    "print('\\nPASS: Score is above P=0.05 (see if you can beat our score of 0.1396)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "e3feac22dae48e9c909667a4e4a753ba",
     "grade": true,
     "grade_id": "1i",
     "locked": true,
     "points": 5,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test with bigger circuits now.\n",
    "# Give a fixed routing so we only test your scheduler\n",
    "_hardware = nx.grid_2d_graph(6, 6)\n",
    "_hardware = nx.relabel_nodes(_hardware, {n: i for i, n in enumerate(_hardware.nodes)})  # Relabel with integer indices\n",
    "_routed = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[36];\\ncx q[0],q[1];\\ncx q[0],q[6];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(pi/4) q[6];\\nrz(-pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\ncx q[2],q[3];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(pi/4) q[3];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\ncx q[8],q[7];\\ncx q[8],q[2];\\nh q[8];\\ncx q[7],q[8];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nrz(pi/4) q[8];\\ncx q[7],q[8];\\nrz(pi/4) q[7];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(2.6375741) q[7];\\nrx(pi) q[7];\\nrz(2.6375741) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(-5*pi/4) q[8];\\nrx(pi/2) q[8];\\nrz(pi/2) q[8];\\ncx q[9],q[10];\\ncx q[9],q[8];\\nh q[9];\\ncx q[10],q[9];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nrz(pi/4) q[9];\\ncx q[10],q[9];\\nrz(pi/4) q[10];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-pi/4) q[10];\\nrz(pi/4) q[8];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(2.6375741) q[10];\\nrx(pi) q[10];\\nrz(2.6375741) q[10];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-5*pi/4) q[9];\\nrx(pi/2) q[9];\\nrz(pi/2) q[9];\\ncx q[15],q[14];\\ncx q[15],q[9];\\nh q[15];\\ncx q[14],q[15];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nrz(pi/4) q[15];\\ncx q[14],q[15];\\nrz(pi/4) q[14];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-pi/4) q[14];\\nrz(pi/4) q[9];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(2.6375741) q[14];\\nrx(pi) q[14];\\nrz(2.6375741) q[14];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-5*pi/4) q[15];\\nrx(pi/2) q[15];\\nrz(pi/2) q[15];\\ncx q[16],q[17];\\ncx q[16],q[15];\\nh q[16];\\ncx q[17],q[16];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nrz(pi/4) q[16];\\ncx q[17],q[16];\\nrz(pi/4) q[17];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-pi/4) q[17];\\nrz(pi/4) q[15];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(2.6375741) q[17];\\nrx(pi) q[17];\\nrz(2.6375741) q[17];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-5*pi/4) q[16];\\nrx(pi/2) q[16];\\nrz(pi/2) q[16];\\ncx q[22],q[21];\\ncx q[22],q[16];\\nh q[22];\\ncx q[21],q[22];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nrz(pi/4) q[22];\\ncx q[21],q[22];\\nrz(pi/4) q[21];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-pi/4) q[21];\\nrz(pi/4) q[16];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(2.6375741) q[21];\\nrx(pi) q[21];\\nrz(2.6375741) q[21];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-5*pi/4) q[22];\\nrx(pi/2) q[22];\\nrz(pi/2) q[22];\\ncx q[23],q[29];\\ncx q[23],q[22];\\nh q[23];\\ncx q[29],q[23];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nrz(pi/4) q[23];\\ncx q[29],q[23];\\nrz(pi/4) q[29];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-pi/4) q[29];\\nrz(pi/4) q[22];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(2.6375741) q[29];\\nrx(pi) q[29];\\nrz(2.6375741) q[29];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-5*pi/4) q[23];\\nrx(pi/2) q[23];\\nrz(pi/2) q[23];\\ncx q[11],q[5];\\nswap q[17],q[11];\\ncx q[17],q[23];\\nswap q[17],q[11];\\nh q[11];\\ncx q[5],q[11];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nrz(pi/4) q[11];\\ncx q[5],q[11];\\nrz(pi/4) q[5];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-pi/4) q[5];\\nrz(pi/4) q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-5*pi/4) q[11];\\nrx(pi/2) q[11];\\nrz(pi/2) q[11];\\nswap q[10],q[4];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[4];\\nswap q[10],q[4];\\ncx q[10],q[11];\\nswap q[10],q[4];\\nh q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(pi/4) q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(pi/4) q[28];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(-5*pi/4) q[4];\\nrx(pi/2) q[4];\\nrz(pi/2) q[4];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(-pi/4) q[28];\\nrz(pi/4) q[11];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(2.6375741) q[28];\\nrx(pi) q[28];\\nrz(2.6375741) q[28];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\ncx q[13],q[12];\\nswap q[7],q[13];\\nswap q[1],q[7];\\nswap q[2],q[1];\\nswap q[3],q[2];\\ncx q[3],q[4];\\nswap q[3],q[2];\\nswap q[2],q[1];\\nswap q[1],q[7];\\nswap q[7],q[13];\\nh q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(-5*pi/4) q[13];\\nrx(pi/2) q[13];\\nrz(pi/2) q[13];\\nrz(pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(2.6375741) q[12];\\nrx(pi) q[12];\\nrz(2.6375741) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\ncx q[19],q[18];\\ncx q[19],q[13];\\nh q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(pi/4) q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(-5*pi/4) q[19];\\nrx(pi/2) q[19];\\nrz(pi/2) q[19];\\nrz(pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(pi/4) q[13];\\nrz(-pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(2.6375741) q[18];\\nrx(pi) q[18];\\nrz(2.6375741) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\ncx q[20],q[26];\\ncx q[20],q[19];\\nh q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(pi/4) q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(-5*pi/4) q[20];\\nrx(pi/2) q[20];\\nrz(pi/2) q[20];\\nrz(pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(pi/4) q[19];\\nrz(-pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(2.6375741) q[26];\\nrx(pi) q[26];\\nrz(2.6375741) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\ncx q[25],q[24];\\nswap q[19],q[25];\\ncx q[19],q[20];\\nswap q[19],q[25];\\nh q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(pi/4) q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(-5*pi/4) q[25];\\nrx(pi/2) q[25];\\nrz(pi/2) q[25];\\nrz(pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(pi/4) q[20];\\nrz(-pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(2.6375741) q[24];\\nrx(pi) q[24];\\nrz(2.6375741) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\ncx q[31],q[30];\\ncx q[31],q[25];\\nh q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(pi/4) q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(-5*pi/4) q[31];\\nrx(pi/2) q[31];\\nrz(pi/2) q[31];\\nrz(pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(pi/4) q[25];\\nrz(-pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(2.6375741) q[30];\\nrx(pi) q[30];\\nrz(2.6375741) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\ncx q[32],q[33];\\ncx q[32],q[31];\\nh q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(pi/4) q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(-5*pi/4) q[32];\\nrx(pi/2) q[32];\\nrz(pi/2) q[32];\\nrz(pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(pi/4) q[31];\\nrz(-pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(2.6375741) q[33];\\nrx(pi) q[33];\\nrz(2.6375741) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nswap q[33],q[27];\\ncx q[33],q[34];\\nswap q[33],q[27];\\nswap q[26],q[27];\\ncx q[26],q[32];\\nswap q[26],q[27];\\nh q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(pi/4) q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(-5*pi/4) q[27];\\nrx(pi/2) q[27];\\nrz(pi/2) q[27];\\nrz(pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(pi/4) q[32];\\nrz(-pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(2.6375741) q[34];\\nrx(pi) q[34];\\nrz(2.6375741) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nswap q[33],q[27];\\nswap q[34],q[33];\\ncx q[34],q[35];\\nswap q[34],q[33];\\nswap q[33],q[27];\\nh q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(pi/4) q[27];\\nswap q[28],q[34];\\ncx q[28],q[27];\\nswap q[28],q[34];\\nrz(-pi/4) q[27];\\nswap q[26],q[32];\\ncx q[26],q[27];\\nswap q[26],q[32];\\nrz(-5*pi/4) q[27];\\nrx(pi/2) q[27];\\nrz(pi/2) q[27];\\nrz(pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nrz(pi/4) q[32];\\nrz(-pi/4) q[34];\\nswap q[33],q[32];\\ncx q[33],q[34];\\nswap q[33],q[32];\\nswap q[26],q[27];\\ncx q[26],q[32];\\nswap q[26],q[27];\\nh q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(pi/4) q[32];\\ncx q[33],q[32];\\nrz(-pi/4) q[32];\\ncx q[31],q[32];\\nrz(-5*pi/4) q[32];\\nrx(pi/2) q[32];\\nrz(pi/2) q[32];\\nrz(pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\nrz(pi/4) q[31];\\nrz(-pi/4) q[33];\\nswap q[32],q[31];\\ncx q[32],q[33];\\nswap q[32],q[31];\\ncx q[32],q[31];\\nh q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(pi/4) q[31];\\ncx q[30],q[31];\\nrz(-pi/4) q[31];\\ncx q[25],q[31];\\nrz(-5*pi/4) q[31];\\nrx(pi/2) q[31];\\nrz(pi/2) q[31];\\nrz(pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\nrz(pi/4) q[25];\\nrz(-pi/4) q[30];\\nswap q[24],q[25];\\ncx q[24],q[30];\\nswap q[24],q[25];\\ncx q[31],q[25];\\nh q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(pi/4) q[25];\\ncx q[24],q[25];\\nrz(-pi/4) q[25];\\nswap q[19],q[20];\\ncx q[19],q[25];\\nswap q[19],q[20];\\nrz(-5*pi/4) q[25];\\nrx(pi/2) q[25];\\nrz(pi/2) q[25];\\nrz(pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nrz(pi/4) q[20];\\nrz(-pi/4) q[24];\\nswap q[19],q[20];\\nswap q[18],q[19];\\ncx q[18],q[24];\\nswap q[18],q[19];\\nswap q[19],q[20];\\nswap q[19],q[25];\\ncx q[19],q[20];\\nswap q[19],q[25];\\nh q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(pi/4) q[20];\\ncx q[26],q[20];\\nrz(-pi/4) q[20];\\ncx q[19],q[20];\\nrz(-5*pi/4) q[20];\\nrx(pi/2) q[20];\\nrz(pi/2) q[20];\\nrz(pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\nrz(pi/4) q[19];\\nrz(-pi/4) q[26];\\nswap q[25],q[19];\\ncx q[25],q[26];\\nswap q[25],q[19];\\ncx q[20],q[19];\\nh q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(pi/4) q[19];\\ncx q[18],q[19];\\nrz(-pi/4) q[19];\\ncx q[13],q[19];\\nrz(-5*pi/4) q[19];\\nrx(pi/2) q[19];\\nrz(pi/2) q[19];\\nrz(pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\nrz(pi/4) q[13];\\nrz(-pi/4) q[18];\\nswap q[12],q[13];\\ncx q[12],q[18];\\nswap q[12],q[13];\\ncx q[19],q[13];\\nh q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[13];\\ncx q[12],q[13];\\nrz(-pi/4) q[13];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[7],q[1];\\ncx q[7],q[13];\\nswap q[7],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(-5*pi/4) q[13];\\nrx(pi/2) q[13];\\nrz(pi/2) q[13];\\nrz(pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nrz(pi/4) q[4];\\nrz(-pi/4) q[12];\\nswap q[3],q[4];\\nswap q[2],q[3];\\nswap q[1],q[2];\\nswap q[0],q[1];\\nswap q[6],q[0];\\ncx q[6],q[12];\\nswap q[6],q[0];\\nswap q[0],q[1];\\nswap q[1],q[2];\\nswap q[2],q[3];\\nswap q[3],q[4];\\nswap q[7],q[13];\\nswap q[1],q[7];\\nswap q[2],q[1];\\nswap q[3],q[2];\\ncx q[3],q[4];\\nswap q[3],q[2];\\nswap q[2],q[1];\\nswap q[1],q[7];\\nswap q[7],q[13];\\nh q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(2.6375741) q[12];\\nrx(pi) q[12];\\nrz(2.6375741) q[12];\\ncx q[13],q[12];\\nrz(2.6375741) q[18];\\nrx(pi) q[18];\\nrz(2.6375741) q[18];\\ncx q[19],q[18];\\nrz(2.6375741) q[26];\\nrx(pi) q[26];\\nrz(2.6375741) q[26];\\ncx q[20],q[26];\\nrz(2.6375741) q[24];\\nrx(pi) q[24];\\nrz(2.6375741) q[24];\\ncx q[25],q[24];\\nrz(2.6375741) q[30];\\nrx(pi) q[30];\\nrz(2.6375741) q[30];\\ncx q[31],q[30];\\nrz(2.6375741) q[33];\\nrx(pi) q[33];\\nrz(2.6375741) q[33];\\ncx q[32],q[33];\\nrz(2.6375741) q[34];\\nrx(pi) q[34];\\nrz(2.6375741) q[34];\\nswap q[33],q[27];\\ncx q[33],q[34];\\nswap q[33],q[27];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(pi/4) q[4];\\nswap q[22],q[28];\\nswap q[16],q[22];\\nswap q[10],q[16];\\ncx q[10],q[4];\\nswap q[10],q[16];\\nswap q[16],q[22];\\nswap q[22],q[28];\\nrz(-pi/4) q[4];\\nrz(pi/4) q[28];\\nswap q[5],q[11];\\ncx q[5],q[4];\\nswap q[5],q[11];\\nrz(-5*pi/4) q[4];\\nrx(pi/2) q[4];\\nrz(pi/2) q[4];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nrz(-pi/4) q[28];\\nrz(pi/4) q[11];\\nswap q[10],q[11];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[11];\\nswap q[10],q[4];\\ncx q[10],q[11];\\nswap q[10],q[4];\\nrz(2.6375741) q[28];\\nrx(pi) q[28];\\nrz(2.6375741) q[28];\\nswap q[10],q[4];\\nswap q[16],q[10];\\nswap q[22],q[16];\\ncx q[22],q[28];\\nswap q[22],q[16];\\nswap q[16],q[10];\\nswap q[10],q[4];\\nh q[11];\\ncx q[5],q[11];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nrz(pi/4) q[11];\\ncx q[5],q[11];\\nrz(pi/4) q[5];\\nrz(-pi/4) q[11];\\nswap q[17],q[23];\\ncx q[17],q[11];\\nswap q[17],q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(-pi/4) q[5];\\nrz(pi/4) q[23];\\nswap q[17],q[23];\\nswap q[11],q[17];\\ncx q[11],q[5];\\nswap q[11],q[17];\\nswap q[17],q[23];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\nrz(-5*pi/4) q[11];\\nrx(pi/2) q[11];\\nrz(pi/2) q[11];\\nswap q[17],q[11];\\ncx q[17],q[23];\\nswap q[17],q[11];\\nh q[23];\\ncx q[29],q[23];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nrz(pi/4) q[23];\\ncx q[29],q[23];\\nrz(pi/4) q[29];\\nrz(-pi/4) q[23];\\ncx q[22],q[23];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(-pi/4) q[29];\\nrz(pi/4) q[22];\\nswap q[28],q[22];\\ncx q[28],q[29];\\nswap q[28],q[22];\\nrz(2.6375741) q[29];\\nrx(pi) q[29];\\nrz(2.6375741) q[29];\\nrz(-5*pi/4) q[23];\\nrx(pi/2) q[23];\\nrz(pi/2) q[23];\\ncx q[23],q[22];\\nh q[22];\\ncx q[21],q[22];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nrz(pi/4) q[22];\\ncx q[21],q[22];\\nrz(pi/4) q[21];\\nrz(-pi/4) q[22];\\ncx q[16],q[22];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(-pi/4) q[21];\\nrz(pi/4) q[16];\\nswap q[15],q[16];\\ncx q[15],q[21];\\nswap q[15],q[16];\\nrz(2.6375741) q[21];\\nrx(pi) q[21];\\nrz(2.6375741) q[21];\\nrz(-5*pi/4) q[22];\\nrx(pi/2) q[22];\\nrz(pi/2) q[22];\\ncx q[22],q[16];\\nh q[16];\\ncx q[17],q[16];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nrz(pi/4) q[16];\\ncx q[17],q[16];\\nrz(pi/4) q[17];\\nrz(-pi/4) q[16];\\ncx q[15],q[16];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(-pi/4) q[17];\\nrz(pi/4) q[15];\\nswap q[16],q[15];\\ncx q[16],q[17];\\nswap q[16],q[15];\\nrz(2.6375741) q[17];\\nrx(pi) q[17];\\nrz(2.6375741) q[17];\\nrz(-5*pi/4) q[16];\\nrx(pi/2) q[16];\\nrz(pi/2) q[16];\\ncx q[16],q[15];\\nh q[15];\\ncx q[14],q[15];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nrz(pi/4) q[15];\\ncx q[14],q[15];\\nrz(pi/4) q[14];\\nrz(-pi/4) q[15];\\ncx q[9],q[15];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(-pi/4) q[14];\\nrz(pi/4) q[9];\\nswap q[8],q[9];\\ncx q[8],q[14];\\nswap q[8],q[9];\\nrz(2.6375741) q[14];\\nrx(pi) q[14];\\nrz(2.6375741) q[14];\\nrz(-5*pi/4) q[15];\\nrx(pi/2) q[15];\\nrz(pi/2) q[15];\\ncx q[15],q[9];\\nh q[9];\\ncx q[10],q[9];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nrz(pi/4) q[9];\\ncx q[10],q[9];\\nrz(pi/4) q[10];\\nrz(-pi/4) q[9];\\ncx q[8],q[9];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(-pi/4) q[10];\\nrz(pi/4) q[8];\\nswap q[9],q[8];\\ncx q[9],q[10];\\nswap q[9],q[8];\\nrz(2.6375741) q[10];\\nrx(pi) q[10];\\nrz(2.6375741) q[10];\\nrz(-5*pi/4) q[9];\\nrx(pi/2) q[9];\\nrz(pi/2) q[9];\\ncx q[9],q[8];\\nh q[8];\\ncx q[7],q[8];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nrz(pi/4) q[8];\\ncx q[7],q[8];\\nrz(pi/4) q[7];\\nrz(-pi/4) q[8];\\ncx q[2],q[8];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[7];\\nswap q[1],q[2];\\ncx q[1],q[7];\\nswap q[1],q[2];\\nrz(2.6375741) q[7];\\nrx(pi) q[7];\\nrz(2.6375741) q[7];\\nrz(-5*pi/4) q[8];\\nrx(pi/2) q[8];\\nrz(pi/2) q[8];\\ncx q[8],q[2];\\nh q[2];\\ncx q[3],q[2];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nrz(pi/4) q[2];\\ncx q[3],q[2];\\nrz(pi/4) q[3];\\nrz(-pi/4) q[2];\\nswap q[1],q[0];\\ncx q[1],q[2];\\nswap q[1],q[0];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[3];\\nswap q[1],q[0];\\nswap q[2],q[1];\\ncx q[2],q[3];\\nswap q[2],q[1];\\nswap q[1],q[0];\\nrz(2.6375741) q[3];\\nrx(pi) q[3];\\nrz(2.6375741) q[3];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nswap q[1],q[2];\\ncx q[1],q[0];\\nswap q[1],q[2];\\nh q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(pi/4) q[0];\\ncx q[1],q[0];\\nrz(-pi/4) q[0];\\ncx q[6],q[0];\\nrz(-5*pi/4) q[0];\\nrx(pi/2) q[0];\\nrz(pi/2) q[0];\\nrz(pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\nrz(pi/4) q[6];\\nrz(-pi/4) q[1];\\nswap q[0],q[6];\\ncx q[0],q[1];\\nswap q[0],q[6];\\ncx q[0],q[6];\\nrz(2.6375741) q[1];\\nrx(pi) q[1];\\nrz(2.6375741) q[1];\\ncx q[0],q[1];\\ncx q[2],q[3];\\ncx q[8],q[7];\\ncx q[9],q[10];\\ncx q[15],q[14];\\ncx q[16],q[17];\\ncx q[22],q[21];\\ncx q[23],q[29];\\ncx q[11],q[5];\\n')\n",
    "_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(408458)\n",
    "_t1_times = {\n",
    "    i: np.random.randint(1e6, 3e6)\n",
    "    for i in _hardware.nodes\n",
    "}\n",
    "_xtalk_errors = {\n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.985, 0.999)\n",
    "    for e1, e2 in itertools.combinations(_hardware.edges, 2)\n",
    "}\n",
    "_schedule = schedule_circuit(_routed, _gate_times, _t1_times, _xtalk_errors)\n",
    "validate_schedule(_routed, _gate_times, _schedule, check_unitary=False)\n",
    "_score, _worst = score_schedule(_routed, _gate_times, _schedule, _t1_times, _xtalk_errors)\n",
    "assert 0.08678814 <= _worst <= 0.08678815, 'internal error'\n",
    "assert _score >= 0.10, 'score is below P=0.10, try improving the schedule'\n",
    "print('\\nPASS: Score is above P=0.10 (see if you can beat our score of 0.1396)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Bonus Challenge (not graded)\n",
    "\n",
    "If you would like to practice making a better noise-aware compiler, compete against your classmates to compile the best circuit.\n",
    "\n",
    "You may improve your mapping, routing, and scheduling solutions above.  They should still pass the tests but you should make them produce more efficient solutions than those parts require.\n",
    "\n",
    "The test below will give you an \"Estimated Success Rate\" score.  The winner will receive extra credit (the student with the highest Estimated Success Rate i.e. closest to 1).  You may tell the score you achieve to your classmates.\n",
    "\n",
    "Compilation Rules: Any circuit and schedule are allowed as long as it is an equivalent circuit.  This allows for routing strategies other than swap-there-and-back and scheduling strategies like as-late-as-possible.  Also, ANY other circuit optimizations are allowed but you may not use Qiskit's (or another tool's) optimization, mapping, routing, or scheduling passes (except decomposing with simply `qiskit.transpile(circuit, basis_gates=[...])`).  To aid in verification, make sure all qubits are swapped back to the inital mapping at the end of the circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "d9c95f9406940ec3e9b789a56dd1f4f3",
     "grade": false,
     "grade_id": "2",
     "locked": false,
     "schema_version": 3,
     "solution": true,
     "task": false
    }
   },
   "outputs": [],
   "source": [
    "def compile_circuit(quantum_circuit, target_hardware, gate_times, t1_times, xtalk_errors):\n",
    "    '''Optimizes, mappes, routes, and schedules a circuit, returning the final schedule.'''\n",
    "    ### Just improve your implementations of\n",
    "    ### map_circuit, route_circuit, and schedule_circuit.\n",
    "    ### You may modify this function if you want to add additional optimizations.\n",
    "    ### You may add the arguments t1_times and/or xtalk_errors to\n",
    "    ### map_circuit and route_circuit as well\n",
    "    # YOUR CODE HERE\n",
    "    mapping = map_circuit(quantum_circuit, target_hardware)\n",
    "    routed = route_circuit(quantum_circuit, target_hardware, mapping)\n",
    "    schedule = schedule_circuit(routed, gate_times, t1_times, xtalk_errors)\n",
    "    return mapping, schedule  # Return the mapping to help with verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scratch space (add more cells if you need)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "ea862780f340f4b63dd1f8422f6362d8",
     "grade": true,
     "grade_id": "2-test",
     "locked": true,
     "points": 0,
     "schema_version": 3,
     "solution": false,
     "task": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Do not delete this cell\n",
    "# The autograder will test the function compile_circuit and give a challenge score.\n",
    "\n",
    "comp_hardware1 = nx.grid_2d_graph(4, 2)\n",
    "comp_hardware1 = nx.relabel_nodes(comp_hardware1,\n",
    "    {n: i for i, n in enumerate(comp_hardware1.nodes)})\n",
    "comp_hardware = nx.grid_2d_graph(8, 8)\n",
    "comp_hardware = nx.relabel_nodes(comp_hardware,\n",
    "    {n: i for i, n in enumerate(comp_hardware.nodes)})\n",
    "comp_gate_times = {\n",
    "    '1' : 12.0,\n",
    "    'rz' : 0.0,  # Free\n",
    "    'cx' : 80.0,\n",
    "}\n",
    "np.random.seed(408458)\n",
    "comp_t1_times1 = {\n",
    "    i: np.random.randint(1e6, 3e6)\n",
    "    for i in comp_hardware1.nodes\n",
    "}\n",
    "comp_t1_times = {\n",
    "    i: np.random.randint(1e6, 3e6)\n",
    "    for i in comp_hardware.nodes\n",
    "}\n",
    "comp_xtalk_errors1 = {\n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.985, 0.999)\n",
    "    for e1, e2 in itertools.combinations(comp_hardware1.edges, 2)\n",
    "}\n",
    "comp_xtalk_errors = {\n",
    "    tuple(sorted([e1, e2])): np.random.uniform(0.985, 0.999)\n",
    "    for e1, e2 in itertools.combinations(comp_hardware.edges, 2)\n",
    "}\n",
    "#comp_circuit1 = decomposed_adder_circuit(8)\n",
    "comp_circuit1 = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[1],q[4];\\ncx q[1],q[0];\\nh q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(pi/4) q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(-5*pi/4) q[1];\\nrx(pi/2) q[1];\\nrz(pi/2) q[1];\\nrz(pi/4) q[4];\\ncx q[0],q[4];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[4];\\nrx(pi) q[4];\\nrz(2.6375741) q[4];\\ncx q[0],q[4];\\ncx q[2],q[5];\\ncx q[2],q[1];\\nh q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(pi/4) q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[5];\\ncx q[1],q[5];\\nrz(pi/4) q[1];\\nrz(-pi/4) q[5];\\ncx q[1],q[5];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[1],q[5];\\ncx q[3],q[6];\\ncx q[3],q[2];\\nh q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(pi/4) q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(-5*pi/4) q[3];\\nrx(pi/2) q[3];\\nrz(pi/2) q[3];\\nrz(pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(2.6375741) q[6];\\nrx(pi) q[6];\\nrz(2.6375741) q[6];\\ncx q[2],q[6];\\ncx q[3],q[7];\\nh q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(pi/4) q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(-5*pi/4) q[3];\\nrx(pi/2) q[3];\\nrz(pi/2) q[3];\\nrz(pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\ncx q[3],q[2];\\nh q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(pi/4) q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[5];\\ncx q[1],q[5];\\nrz(pi/4) q[1];\\nrz(-pi/4) q[5];\\ncx q[1],q[5];\\ncx q[2],q[1];\\nh q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(pi/4) q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(-5*pi/4) q[1];\\nrx(pi/2) q[1];\\nrz(pi/2) q[1];\\nrz(pi/4) q[4];\\ncx q[0],q[4];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[4];\\ncx q[0],q[4];\\ncx q[1],q[0];\\nrz(2.6375741) q[4];\\nrx(pi) q[4];\\nrz(2.6375741) q[4];\\ncx q[1],q[4];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[2],q[5];\\nrz(2.6375741) q[6];\\nrx(pi) q[6];\\nrz(2.6375741) q[6];\\ncx q[3],q[6];\\n')\n",
    "comp_circuit = decomposed_adder_circuit(64)\n",
    "#comp_circuit = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[8];\\ncx q[1],q[4];\\ncx q[1],q[0];\\nh q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(pi/4) q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(-5*pi/4) q[1];\\nrx(pi/2) q[1];\\nrz(pi/2) q[1];\\nrz(pi/4) q[4];\\ncx q[0],q[4];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[4];\\ncx q[0],q[4];\\nrz(2.6375741) q[4];\\nrx(pi) q[4];\\nrz(2.6375741) q[4];\\ncx q[0],q[4];\\ncx q[2],q[5];\\ncx q[2],q[1];\\nh q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(pi/4) q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[5];\\ncx q[1],q[5];\\nrz(pi/4) q[1];\\nrz(-pi/4) q[5];\\ncx q[1],q[5];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[1],q[5];\\ncx q[3],q[6];\\ncx q[3],q[2];\\nh q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(pi/4) q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(-5*pi/4) q[3];\\nrx(pi/2) q[3];\\nrz(pi/2) q[3];\\nrz(pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\nrz(2.6375741) q[6];\\nrx(pi) q[6];\\nrz(2.6375741) q[6];\\ncx q[2],q[6];\\ncx q[3],q[7];\\nh q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(pi/4) q[3];\\ncx q[6],q[3];\\nrz(-pi/4) q[3];\\ncx q[2],q[3];\\nrz(-5*pi/4) q[3];\\nrx(pi/2) q[3];\\nrz(pi/2) q[3];\\nrz(pi/4) q[6];\\ncx q[2],q[6];\\nrz(pi/4) q[2];\\nrz(-pi/4) q[6];\\ncx q[2],q[6];\\ncx q[3],q[2];\\nh q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(pi/4) q[2];\\ncx q[5],q[2];\\nrz(-pi/4) q[2];\\ncx q[1],q[2];\\nrz(-5*pi/4) q[2];\\nrx(pi/2) q[2];\\nrz(pi/2) q[2];\\nrz(pi/4) q[5];\\ncx q[1],q[5];\\nrz(pi/4) q[1];\\nrz(-pi/4) q[5];\\ncx q[1],q[5];\\ncx q[2],q[1];\\nh q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(pi/4) q[1];\\ncx q[4],q[1];\\nrz(-pi/4) q[1];\\ncx q[0],q[1];\\nrz(-5*pi/4) q[1];\\nrx(pi/2) q[1];\\nrz(pi/2) q[1];\\nrz(pi/4) q[4];\\ncx q[0],q[4];\\nrz(pi/4) q[0];\\nrz(-pi/4) q[4];\\ncx q[0],q[4];\\ncx q[1],q[0];\\nrz(2.6375741) q[4];\\nrx(pi) q[4];\\nrz(2.6375741) q[4];\\ncx q[1],q[4];\\nrz(2.6375741) q[5];\\nrx(pi) q[5];\\nrz(2.6375741) q[5];\\ncx q[2],q[5];\\nrz(2.6375741) q[6];\\nrx(pi) q[6];\\nrz(2.6375741) q[6];\\ncx q[3],q[6];\\n')\n",
    "\n",
    "# Test your code on a small circuit\n",
    "print('Small test')\n",
    "_mapping1, _schedule1 = compile_circuit(comp_circuit1, comp_hardware1, comp_gate_times,\n",
    "                                      comp_t1_times1, comp_xtalk_errors1)\n",
    "_routed1 = schedule_to_circuit(_schedule1)\n",
    "validate_schedule(_routed1, comp_gate_times, _schedule1, check_unitary=True)\n",
    "_score1, _worst1 = score_schedule(_routed1, comp_gate_times, _schedule1,\n",
    "                                  comp_t1_times1, comp_xtalk_errors1)\n",
    "assert 0.90215595 <= _worst1 <= 0.90215596, 'internal error'\n",
    "print('=====\\n')\n",
    "\n",
    "# Run your code on the actual circuit\n",
    "print('Challenge test')\n",
    "_mapping, _schedule = compile_circuit(comp_circuit, comp_hardware, comp_gate_times,\n",
    "                                      comp_t1_times, comp_xtalk_errors)\n",
    "_routed = schedule_to_circuit(_schedule)\n",
    "validate_schedule(_routed, comp_gate_times, _schedule, check_unitary=False)\n",
    "_score, _worst = score_schedule(_routed, comp_gate_times, _schedule,\n",
    "                                comp_t1_times, comp_xtalk_errors)\n",
    "assert 0.00024464483 <= _worst <= 0.00024464484, 'internal error'\n",
    "print('=====\\n')\n",
    "\n",
    "print(f'Your Estimated Success Rate Score: {_score}')"
   ]
  }
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