ZNE demo using catalyst

.github/workflows/build-branch.yml

@@ -317,6 +317,11 @@ jobs:
           source ${{ steps.venv.outputs.location }}/bin/activate
           make POETRY_BIN="${{ steps.poetry.outputs.poetry_bin }}" UPGRADE_PL="${{ needs.compute-build-strategy-matrix.outputs.build-environment == 'dev' }}" environment
 
+      - name: Install OpenCL
+        run: |
+          sudo apt update
+          sudo apt install ocl-icd-opencl-dev
+
       - name: Download Worker Load Data Artifact
         uses: actions/download-artifact@v3
         with:

demonstrations/tutorial_zne_catalyst.metadata.json

@@ -0,0 +1,57 @@
+{
+    "title": "Digital zero-noise extrapolation with Catalyst",
+    "authors": [
+        {
+            "username": "cosenal"
+        },
+        {
+            "username": "natestemen"
+        }
+    ],
+    "dateOfPublication": "2024-09-09T00:00:00+00:00",
+    "dateOfLastModification": "2024-09-09T00:00:00+00:00",
+    "categories": [
+        "Algorithms",
+        "Quantum Computing"
+    ],
+    "tags": [],
+    "previewImages": [
+        {
+            "type": "thumbnail",
+            "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_zne_catalyst.png"
+        },
+        {
+            "type": "large_thumbnail",
+            "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_zne_catalyst.png"
+        }
+    ],
+    "seoDescription": "Learn to use error mitigation and the digital zero-noise extrapolation (ZNE) technique with Catalyst, the PennyLane framework for quantum JIT compilation.",
+    "doi": "",
+    "references": [
+        {
+            "id": "DZNEpaper",
+            "type": "preprint",
+            "title": "Digital zero noise extrapolation for quantum error mitigation",
+            "authors": "Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng",
+            "year": "2020",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.2005.10921",
+            "url": "https://arxiv.org/abs/2005.10921v2"
+        }
+    ],
+    "basedOnPapers": [],
+    "referencedByPapers": [],
+    "relatedContent": [
+        {
+            "type": "demonstration",
+            "id": "tutorial_error_mitigation",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "qrack",
+            "weight": 1.0
+        }
+    ]
+}

demonstrations/tutorial_zne_catalyst.py

@@ -0,0 +1,274 @@
+r"""
+Digital zero-noise extrapolation with Catalyst
+==============================================
+
+In this tutorial, you will learn how to use error mitigation, and in particular 
+the zero-noise extrapolation (ZNE) technique, in combination with 
+`Catalyst <https://docs.pennylane.ai/projects/catalyst>`_, a framework for quantum 
+just-in-time (JIT) compilation with PennyLane. 
+We'll demonstrate how to generate noise-scaled circuits, execute them on a noisy quantum
+simulator, and use extrapolation techniques to estimate the zero-noise result, all while
+leveraging JIT compilation through Catalyst.
+
+.. image:: ../_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_zne_catalyst.png
+    :width: 65%
+    :align: center
+
+The demo :doc:`Error mitigation with Mitiq and PennyLane <tutorial_error_mitigation>`
+shows how ZNE, along with other error mitigation techniques, can be carried out in PennyLane
+by using `Mitiq <https://github.com/unitaryfund/mitiq>`__, a Python library developed by `Unitary Fund <https://unitary.fund/>`__.
+
+ZNE in particular is also offered out of the box in PennyLane as a *differentiable* error mitigation technique,
+for usage in combination with variational workflows. More on this in the tutorial
+:doc:`Differentiating quantum error mitigation transforms <tutorial_diffable-mitigation>`.
+
+On top of the error mitigation routines offered in PennyLane, ZNE is also available for just-in-time
+(JIT) compilation. In this tutorial we see how an error mitigation routine can be
+integrated in a Catalyst workflow.
+
+At the end of the tutorial, we will compare the execution time of ZNE routines in
+pure PennyLane vs. PennyLane and Catalyst with JIT.
+
+What is zero-noise extrapolation (ZNE)
+-----------
+Zero-noise extrapolation (ZNE) is a technique used to mitigate the effect of noise on quantum
+computations. First introduced in [#temme2017zne]_, it helps improve the accuracy of quantum
+results by running circuits at varying noise levels and extrapolating back to a hypothetical
+zero-noise case. While this tutorial won't delve into the theory behind ZNE in detail (for which we
+recommend reading the `Mitiq docs <https://mitiq.readthedocs.io/en/stable/guide/zne-5-theory.html>`_
+and the references therein), let's first review what happens when using the protocol in practice.
+
+Stage 1: Generating noise-scaled circuits
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ZNE works by generating circuits with **increased** noise. Catalyst implements the unitary folding
+framework introduced in [#DZNEpaper]_ for generating noise-scaled circuits. In particular, 
+the following two methods are available:
+
+1. **Global folding**: If a circuit implements a global unitary :math:`U`, global folding applies
+   :math:`U(U^\dagger U)^n` for some integer :math:`n`,
+   effectively scaling the noise in the entire circuit.
+2. **Local folding**: Individual gates are repeated (or folded) in contrast with the entire
+   circuit.
+
+Stage 2: Running the circuits
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Once noise-scaled circuits are created, they need to be run! These can be executed on either real
+quantum hardware or a noisy quantum simulator. In this tutorial, we'll use the
+`Qrack quantum simulator <https://qrack.readthedocs.io/>`_, which is both compatible with Catalyst,
+and implements a noise model. For more about the integration of Qrack and Catalyst, see
+the demo :doc:`QJIT compilation with Qrack and Catalyst <qrack>`.
+
+Stage 3: Combining the results
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+After executing the noise-scaled circuits, an extrapolation on the results is performed  
+to estimate the zero-noise limit---the result we would expect in a noise-free scenario. 
+Catalyst provides **polynomial** and **exponential** extrapolation methods.
+
+Defining the mirror circuit
+---------------------------
+
+The first step for demoing an error mitigation routine is to define a circuit. 
+Here we build a simple mirror circuit starting off a unitary 2-design. 
+This is a typical construction for a randomized benchmarking circuit, which is used in many tasks
+in quantum computing. Given such circuit, we measure the expectation value :math:`\langle Z\rangle` 
+on the state of the first qubit, and by construction of the circuit, we expect this value to be
+equal to 1.
+"""
+
+import timeit
+
+import numpy as np
+import pennylane as qml
+from catalyst import mitigate_with_zne
+
+n_wires = 5
+
+np.random.seed(42)
+
+n_layers = 10
+template = qml.SimplifiedTwoDesign
+weights_shape = template.shape(n_layers, n_wires)
+w1, w2 = [2 * np.pi * np.random.random(s) for s in weights_shape]
+
+
+def circuit(w1, w2):
+    template(w1, w2, wires=range(n_wires))
+    qml.adjoint(template)(w1, w2, wires=range(n_wires))
+    return qml.expval(qml.PauliZ(0))
+
+
+##############################################################################
+# As a sanity check, we first execute the circuit on the Qrack simulator without any noise.
+
+noiseless_device = qml.device("qrack.simulator", n_wires, noise=0)
+
+ideal_value = qml.QNode(circuit, device=noiseless_device)(w1, w2)
+print(f"Ideal value: {ideal_value}")
+
+##############################################################################
+# In the noiseless scenario, the expectation value of the Pauli-Z measurement
+# is equal to 1, since the first qubit is back in the :math:`|0\rangle` state.
+#
+# Mitigating the noisy circuit
+# ----------------------------
+# Let's now run the circuit through a noisy scenario. The Qrack simulator models noise by
+# applying single-qubit depolarizing noise channels to all qubits in all gates of the circuit.
+# The probability of error is specified by the value of the ``noise`` constructor argument.
+
+NOISE_LEVEL = 0.01
+noisy_device = qml.device("qrack.simulator", n_wires, shots=1000, noise=NOISE_LEVEL)
+
+noisy_qnode = qml.QNode(circuit, device=noisy_device, mcm_method="one-shot")
+noisy_value = noisy_qnode(w1, w2)
+print(f"Error without mitigation: {abs(ideal_value - noisy_value):.3f}")
+
+##############################################################################
+# Again expected, we obtain a noisy value that diverges from the ideal value we obtained above.
+# Fortunately, we have error mitigation to the rescue! We can apply ZNE, however we are still
+# missing some necessary parameters. In particular we still need to specify:
+#
+# 1. The method for scaling this noise up (in Catalyst there are two options: ``global`` and
+#    ``local``).
+# 2. The noise scaling factors (i.e. how much to increase the depth of the circuit).
+# 3. The extrapolation technique used to estimate the ideal value (available in Catalyst are
+#    polynomial and exponential extrapolation).
+#
+# First, we choose a method to scale the noise. This needs to be specified as a Python string.
+
+folding_method = "global"
+
+##############################################################################
+# Next, we pick a list of scale factors. At the time of writing this tutorial,
+# Catalyst supports only odd integer scale factors. In the global folding setting,
+# a scale factor  :math:`s` correspond to the circuit being folded
+# :math:`\frac{s - 1}{2}` times.
+scale_factors = [1, 3, 5]
+
+##############################################################################
+# Finally, we'll choose the extrapolation technique. Both exponential and polynomial extrapolation
+# is available in the :mod:`qml.transforms <pennylane.transforms>` module, and both of these functions can be passed directly
+# into Catalyst's :func:`catalyst.mitigate_with_zne` function. In this tutorial we use polynomial extrapolation,
+# which we hypothesize best models the behavior of the noise scenario we are considering.
+
+from pennylane.transforms import poly_extrapolate
+from functools import partial
+
+extrapolation_method = partial(poly_extrapolate, order=3)
+
+##############################################################################
+# We're now ready to run our example using ZNE with Catalyst! Putting these all together we're able
+# to define a very simple :func:`~.QNode`, which represents the mitigated version of the original circuit.
+
+
+@qml.qjit
+def mitigated_circuit_qjit(w1, w2):
+    return mitigate_with_zne(
+        noisy_qnode,
+        scale_factors=scale_factors,
+        extrapolate=extrapolation_method,
+        folding=folding_method,
+    )(w1, w2)
+
+
+zne_value = mitigated_circuit_qjit(w1, w2)
+
+print(f"Error with ZNE in Catalyst: {abs(ideal_value - zne_value):.3f}")
+
+##############################################################################
+# It's crucial to note that we can use the :func:`~.qjit` decorator here, as all the functions used
+# to define the node are compatible with Catalyst, and we can therefore
+# exploit the potential of just-in-time compilation.
+#
+# Benchmarking
+# ------------
+# For comparison, let's define a very similar :func:`~.qnode`, but this time we don't decorate the node
+# as just-in-time compilable.
+# When it comes to the parameters, the only difference here (due to an implementation technicality)
+# is the type of the ``folding`` argument. Despite the type being different, however,
+# the value of the folding method is the same, i.e., global folding.
+
+
+def mitigated_circuit(w1, w2):
+    return qml.transforms.mitigate_with_zne(
+        noisy_qnode,
+        scale_factors=scale_factors,
+        extrapolate=extrapolation_method,
+        folding=qml.transforms.fold_global,
+    )(w1, w2)
+
+
+zne_value = mitigated_circuit(w1, w2)
+
+print(f"Error with ZNE in PennyLane: {abs(ideal_value - zne_value):.3f}")
+
+##############################################################################
+# To showcase the impact of JIT compilation, let's use Python's ``timeit`` module
+# to measure execution time of ``mitigated_circuit_qjit`` vs. ``mitigated_circuit``:
+
+repeat = 5  # number of timing runs
+number = 5  # number of loops executed in each timing run
+
+times = timeit.repeat("mitigated_circuit(w1, w2)", globals=globals(), number=number, repeat=repeat)
+
+print(f"mitigated_circuit running time (best of {repeat}): {min(times):.3f}s")
+
+times = timeit.repeat(
+    "mitigated_circuit_qjit(w1, w2)", globals=globals(), number=number, repeat=repeat
+)
+
+print(f"mitigated_circuit_qjit running time (best of {repeat}): {min(times):.3f}s")
+
+##############################################################################
+# Already with the simple circuit we started with, and with the simple parameters in our example,
+# we can appreciate the performance differences. That was at the cost of very minimal syntax change.
+#
+# There are still reasons to use ZNE in PennyLane without :func:`~.qjit`, for instance,
+# whenever the device of choice is not supported by Catalyst. To help,
+# we conclude with a landscape of the QEM techniques available on PennyLane.
+#
+# .. list-table::
+#     :widths: 30 20 20 20 20 30
+#     :header-rows: 1
+#
+#     * - **Framework**
+#       - **ZNE folding**
+#       - **ZNE extrapolation**
+#       - **Differentiable**
+#       - **JIT**
+#       - **Other QEM techniques**
+#     * - PennyLane + Mitiq
+#       - global, local, random
+#       - polynomial, exponential
+#       -
+#       -
+#       - PEC, CDR, DDD, REM
+#     * - PennyLane transforms
+#       - global, local
+#       - polynomial, exponential
+#       - ✅
+#       -
+#       -
+#     * - Catalyst (experimental)
+#       - global, local
+#       - polynomial, exponential
+#       - ✅
+#       - ✅
+#       -
+
+
+##############################################################################
+#
+# References
+# ----------
+#
+# .. [#temme2017zne] K. Temme, S. Bravyi, J. M. Gambetta
+#     `"Error Mitigation for Short-Depth Quantum Circuits" <https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.180509>`_,
+#     Phys. Rev. Lett. 119, 180509 (2017).
+#
+# .. [#DZNEpaper]
+#     Tudor Giurgica-Tiron, Yousef Hindy, Ryan LaRose, Andrea Mari, and William J. Zeng
+#     "Digital zero noise extrapolation for quantum error mitigation"
+#     `arXiv:2005.10921v2 <https://arxiv.org/abs/2005.10921v2>`__, 2020.