docs: Add qermit tutorial to docs

docs/index.md

@@ -170,6 +170,13 @@ mitex.get_task_graph()
 
 ## Contents
 
+```{toctree}
+:caption: Overview:
+:maxdepth: 2
+
+tutorial.md
+```
+
 ```{toctree}
 :hidden:
 

docs/tutorial.md

@@ -0,0 +1,207 @@
+---
+file_format: mystnb
+kernelspec:
+  name: python3
+---
+
+# Tutorial
+
+```{code-cell} ipython3
+from pytket import Circuit
+from pytket.circuit.display import render_circuit_jupyter
+
+circ = Circuit(2,2).H(0).CX(0,1).measure_all()
+render_circuit_jupyter(circ)
+```
+
+```{code-cell} ipython3
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def plot_state_probs(state):
+    state_dict = {'State':[i for i in range(len(result_state))], 'Probability':abs(state)**2}
+    state_df = pd.DataFrame(state_dict)
+    sns.catplot(x='State', y='Probability', kind='bar', data=state_df, aspect = 3, height=2)
+    plt.show()
+
+def plot_counts(counts):
+    counts_record = [{"State":str(state), "Count":count} for state, count in counts.items()]
+    count_df = pd.DataFrame().from_records(counts_record)
+    sns.catplot(x='State', y='Count', kind='bar', data=count_df, aspect = 3, height=2)
+    plt.show()
+```
+
+```{code-cell} ipython3
+import qiskit_aer.noise as noise
+
+def depolarizing_noise_model(n_qubits, prob_1, prob_2, prob_ro):
+
+    noise_model = noise.NoiseModel()
+
+    error_2 = noise.depolarizing_error(prob_2, 2)
+    for edge in [[i,j] for i in range(n_qubits) for j in range(i)]:
+        noise_model.add_quantum_error(error_2, ['cx'], [edge[0], edge[1]])
+        noise_model.add_quantum_error(error_2, ['cx'], [edge[1], edge[0]])
+
+    error_1 = noise.depolarizing_error(prob_1, 1)
+    for node in range(n_qubits):
+        noise_model.add_quantum_error(error_1, ['h', 'rx', 'rz', 'u'], [node])
+
+    probabilities = [[1-prob_ro, prob_ro],[prob_ro, 1-prob_ro]]
+    error_ro = noise.ReadoutError(probabilities)
+    for i in range(n_qubits):
+        noise_model.add_readout_error(error_ro, [i])
+
+    return noise_model
+```
+
+```{code-cell} ipython3
+from qermit.taskgraph.mitex import gen_compiled_MitRes
+from pytket.extensions.qiskit import AerBackend
+from qermit import CircuitShots
+
+n_shots = 100000
+noisy_backend = AerBackend(
+    depolarizing_noise_model(5, 0.001, 0.01, 0.05)
+)
+noisy_mitres = gen_compiled_MitRes(noisy_backend, optimisation_level=0)
+
+circ_shots_list = [CircuitShots(circ, n_shots)]
+
+noisy_result_list = noisy_mitres.run(circ_shots_list)
+noisy_result_counts = noisy_result_list[0].get_counts()
+plot_counts(noisy_result_counts)
+```
+
+```{code-cell} ipython3
+noisy_mitres.get_task_graph()
+```
+
+```{code-cell} ipython3
+from qermit.spam import gen_UnCorrelated_SPAM_MitRes
+
+spam_mr = gen_UnCorrelated_SPAM_MitRes(noisy_backend, n_shots)
+spam_result_list = spam_mr.run(circ_shots_list)
+spam_result_counts = spam_result_list[0].get_counts()
+plot_counts(spam_result_counts)
+```
+
+```{code-cell} ipython3
+spam_mr.get_task_graph()
+```
+
+```{code-cell} ipython3
+import numpy as np
+from scipy.stats import unitary_group
+from pytket.circuit import Unitary2qBox
+
+def random_circ(n_qubits: int, depth: int, seed:int = None) -> Circuit:
+
+    np.random.seed(seed)
+
+    c = Circuit(n_qubits)
+
+    for _ in range(depth):
+
+        qubits = np.random.permutation([i for i in range(n_qubits)])
+        qubit_pairs = [[qubits[i], qubits[i + 1]] for i in range(0, n_qubits - 1, 2)]
+
+        for pair in qubit_pairs:
+
+            # Generate random 4x4 unitary matrix.
+            SU4 = unitary_group.rvs(4)  # random unitary in SU4
+            SU4 = SU4 / (np.linalg.det(SU4) ** 0.25)
+            SU4 = np.matrix(SU4)
+
+            # Add gate corresponding to unitary.
+            c.add_unitary2qbox(Unitary2qBox(SU4), *pair)
+
+    return c
+```
+
+```{code-cell} ipython3
+n_qubits = 4
+rand_circ = random_circ(n_qubits,n_qubits,seed=23126)
+render_circuit_jupyter(rand_circ)
+```
+
+```{code-cell} ipython3
+from pytket.utils import QubitPauliOperator
+from qermit import (
+    AnsatzCircuit, SymbolsDict, 
+    ObservableExperiment, ObservableTracker,
+    MitEx
+)
+from pytket.pauli import Pauli, QubitPauliString
+from pytket import Qubit
+
+ideal_backend = AerBackend()
+ideal_mitex = MitEx(ideal_backend)
+
+qps = QubitPauliString(
+    [Qubit(i) for i in range(n_qubits)], 
+    [Pauli.Z for i in range(n_qubits)]
+)
+
+obs_exp = ObservableExperiment(
+    AnsatzCircuit(rand_circ, n_shots, SymbolsDict()),
+    ObservableTracker(QubitPauliOperator({qps:1}))
+)
+obs_exp_list = [obs_exp]
+
+ideal_expectation = ideal_mitex.run(obs_exp_list)
+print(f"Ideal expectation: {ideal_expectation[0]}")
+```
+
+```{code-cell} ipython3
+noisy_mitex = MitEx(noisy_backend)
+
+noisy_expectation = noisy_mitex.run(obs_exp_list)
+print(f"Noisy expectation: {noisy_expectation[0]}")
+```
+
+```{code-cell} ipython3
+from qermit.zero_noise_extrapolation import (
+    gen_ZNE_MitEx, Fit, Folding
+)
+
+zne_me = gen_ZNE_MitEx(
+    backend=noisy_backend, 
+    noise_scaling_list=[9,7,5,3,1], 
+    fit_type=Fit.exponential, 
+    folding_type=Folding.circuit,
+    show_fit=True,
+)
+```
+
+```{code-cell} ipython3
+import seaborn as sns 
+sns.set_style("whitegrid")
+```
+
+```{code-cell} ipython3
+zne_me.get_task_graph()
+```
+
+```{code-cell} ipython3
+zne_me.run(obs_exp_list)
+```
+
+```{code-cell} ipython3
+zne_spam_me = gen_ZNE_MitEx(
+    backend=noisy_backend, 
+    noise_scaling_list=[9,7,5,3,1], 
+    fit_type=Fit.exponential, 
+    show_fit=True,
+    experiment_mitres=spam_mr,
+)
+```
+
+```{code-cell} ipython3
+zne_spam_me.run(obs_exp_list)
+```
+
+```{code-cell} ipython3
+zne_spam_me.get_task_graph()
+```