diff --git a/docs/guides/qpu-information.mdx b/docs/guides/qpu-information.mdx index f9eb5a55d4f..735858502bc 100644 --- a/docs/guides/qpu-information.mdx +++ b/docs/guides/qpu-information.mdx @@ -84,7 +84,12 @@ To access the details page, select [Compute resources](https://quantum.cloud.ibm To find your available QPUs, open the [Compute resources](https://quantum.cloud.ibm.com/computers) page (make sure you are signed in). Note that your selected region might impact the QPUs listed. Click a QPU to view its details. -You can also view your available QPUs by using the [backends API.](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#backends) +You can also view your available QPUs by using the [backends API.](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#backends) For example, the following code will return all of the backends that the specified instance (`my_instance`) can access: + +```python + QiskitRuntimeService(instance="my_instance_CRN") + service.backend() +``` ## View QPU configuration diff --git a/docs/guides/visualize-results.ipynb b/docs/guides/visualize-results.ipynb index c44b184be08..6f06e1b1bd7 100644 --- a/docs/guides/visualize-results.ipynb +++ b/docs/guides/visualize-results.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "1adb4ac8-f331-485b-89f5-e4ec20a6855f", + "id": "7e6b2936-3a18-4917-9c47-30e2d4ce5775", "metadata": {}, "source": [ "# Visualize results" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "6efd5b06-c754-4b34-a14d-e404c9e920cb", + "id": "06a4399f-bca6-4c2e-9128-1494017d0249", "metadata": { "tags": [ "version-info" @@ -32,7 +32,7 @@ }, { "cell_type": "markdown", - "id": "b446771a-caf4-4b59-8e8c-1d115249b818", + "id": "e8d0138c-fd97-49c2-88a5-1929a5a09258", "metadata": {}, "source": [ "## Plot histogram \n", @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "03592c4f-8a0a-4c0b-81f6-658f57d33ee4", + "id": "5cf67f92-a86d-496d-9e13-0d8a841c8dfa", "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2ff2c2d1-185f-4183-b80a-b5b599411245", + "id": "938e8206-d7e8-447d-b798-b7c2507f8901", "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "3b41d6da-1bca-4b82-ac84-b5b811b7d430", + "id": "57d8053e-d030-460d-9c1f-772e53b1a49b", "metadata": {}, "outputs": [ { @@ -123,7 +123,7 @@ }, { "cell_type": "markdown", - "id": "330e0fcb-ce13-4ad2-855e-4e0b8665cf76", + "id": "e3a68e3d-21e6-45a0-bc40-f9a2214dd5b3", "metadata": {}, "source": [ "### Options when plotting a histogram\n", @@ -141,7 +141,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7f456bcc-94a8-4522-9be2-77ea91045c1f", + "id": "bd70e13f-5c52-42fb-8dde-980b15e3604a", "metadata": {}, "outputs": [ { @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "2a3d185d-96ce-4a91-890f-b8e3d7f31eb0", + "id": "019ee04e-0730-4536-94cc-7e2b50d921e1", "metadata": {}, "source": [ "## Plotting estimator results\n", @@ -191,7 +191,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "35e07a46-8423-44ff-abad-7c10c72291a5", + "id": "17c9893a-d1bf-4726-b444-6dce1d56805f", "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "d0e52ff0-96d5-45fb-b5df-38f43fd19ecc", + "id": "a520f049-c2ee-4f14-8039-b5be671f25ae", "metadata": {}, "source": [ "The following cell uses the estimated [standard error](https://en.wikipedia.org/wiki/Standard_error) of each result and adds them as error bars. See the [`bar` plot documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) for a full description of the plot." @@ -273,7 +273,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "6f122049-0b5a-49e8-bde4-d99d91f4b77c", + "id": "4eb79f4b-36b5-4797-a1a0-67d881d46ca4", "metadata": {}, "outputs": [ { diff --git a/docs/tutorials/advanced-techniques-for-qaoa.ipynb b/docs/tutorials/advanced-techniques-for-qaoa.ipynb index 6047c05b4da..57b3d2c83f4 100644 --- a/docs/tutorials/advanced-techniques-for-qaoa.ipynb +++ b/docs/tutorials/advanced-techniques-for-qaoa.ipynb @@ -48,7 +48,6 @@ "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", "- Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)\n", "- Python SAT (`pip install python-sat`)" ] }, diff --git a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb index b01b7343728..7c2cc6ff90a 100644 --- a/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb +++ b/docs/tutorials/approximate-quantum-compilation-for-time-evolution.ipynb @@ -34,8 +34,7 @@ "\n", "* Qiskit SDK v1.0 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n", "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "* AQC-Tensor Qiskit addon (`pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'`)\n", - "* rustworkx v0.15 or later (`pip install rustworkx`)" + "* AQC-Tensor Qiskit addon (`pip install 'qiskit-addon-aqc-tensor[aer,quimb-jax]'`)" ] }, { diff --git a/docs/tutorials/circuit-transpilation-settings.ipynb b/docs/tutorials/circuit-transpilation-settings.ipynb index 9bc964af286..716a09011f2 100644 --- a/docs/tutorials/circuit-transpilation-settings.ipynb +++ b/docs/tutorials/circuit-transpilation-settings.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Compare transpiler settings\n", - "*Usage estimate: under one minute on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -93,7 +93,7 @@ { "data": { "text/plain": [ - "'ibm_kyoto'" + "'ibm_brisbanse'" ] }, "execution_count": 28, diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb index bb6b761f193..fbbd8b4a6da 100644 --- a/docs/tutorials/grovers-algorithm.ipynb +++ b/docs/tutorials/grovers-algorithm.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Grover's algorithm\n", - "*Usage estimate: under one minute on ibm_nairobi (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -114,14 +114,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "6e419a8a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'ibm_cusco'" + "'ibm_brisbane'" ] }, "execution_count": 2, @@ -173,6 +173,54 @@ "oracle.draw(output=\"mpl\", style=\"iqp\")" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7baca7e2-99fc-4089-b5d8-30da56816a6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "marked_states = [\"011\", \"100\"]\n", + "\n", + "oracle = grover_oracle(marked_states)\n", + "oracle.draw(output=\"mpl\", style=\"iqp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d3a26fc9-9090-4527-a749-a412661260b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Output" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "marked_states = [\"011\", \"100\"]\n", + "\n", + "oracle = grover_oracle(marked_states)\n", + "oracle.draw(output=\"mpl\", style=\"iqp\")" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -186,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "283d5265", "metadata": {}, "outputs": [ @@ -196,7 +244,7 @@ "\"Output" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -217,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "f4c3d4b5", "metadata": {}, "outputs": [], @@ -241,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "4933ae44", "metadata": {}, "outputs": [ @@ -251,7 +299,7 @@ "\"Output" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -277,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "c9a3020e", "metadata": {}, "outputs": [ @@ -287,7 +335,7 @@ "\"Output" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -315,7 +363,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "0eb154d4", "metadata": {}, "outputs": [], @@ -338,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "a5ef9913", "metadata": {}, "outputs": [ @@ -348,7 +396,7 @@ "\"Output" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/tutorials/hello-world.ipynb b/docs/tutorials/hello-world.ipynb index 9ede1156780..f8ab6eca469 100644 --- a/docs/tutorials/hello-world.ipynb +++ b/docs/tutorials/hello-world.ipynb @@ -327,8 +327,8 @@ "\n", " # Use the following code instead if you want to run on a simulator:\n", "\n", - " from qiskit_ibm_runtime.fake_provider import FakeAlmadenV2\n", - " backend = FakeAlmadenV2()\n", + " from qiskit_ibm_runtime.fake_provider import FakeFez\n", + " backend = FakeFez()\n", " estimator = Estimator(backend)\n", "\n", " # Convert to an ISA circuit and layout-mapped observables.\n", diff --git a/docs/tutorials/multi-product-formula.ipynb b/docs/tutorials/multi-product-formula.ipynb index 9ddc0a9c301..f379a43befa 100644 --- a/docs/tutorials/multi-product-formula.ipynb +++ b/docs/tutorials/multi-product-formula.ipynb @@ -82,7 +82,6 @@ "* Qiskit Runtime v0.22 or later (`pip install qiskit-ibm-runtime`)\n", "* MPF Qiskit addons (`pip install qiskit_addon_mpf`)\n", "* Qiskit addons utils (`pip install qiskit_addon_utils`)\n", - "* Rustworkx graph library (`pip install rustworkx`)\n", "* Quimb library (`pip install quimb`)\n", "* Qiskit Quimb library (`pip install qiskit-quimb`)\n", "* Numpy v0.21 for compatibility across packages (`pip install numpy==0.21`)" diff --git a/docs/tutorials/operator-back-propagation.ipynb b/docs/tutorials/operator-back-propagation.ipynb index 7d2472c4004..a14b2a317da 100644 --- a/docs/tutorials/operator-back-propagation.ipynb +++ b/docs/tutorials/operator-back-propagation.ipynb @@ -8,7 +8,7 @@ "{/* cspell:ignore simeq // This in an equation and isn't being ignored correctly */}\n", "\n", "# Operator backpropagation (OBP) for estimation of expectation values\n", - "*Usage estimate: 16 minutes on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 16 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -68,8 +68,7 @@ "- Qiskit SDK 1.2 or later (`pip install qiskit`)\n", "- Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`)\n", "- OBP Qiskit addon (`pip install qiskit-addon-obp`)\n", - "- Qiskit addon utils (`pip install qiskit-addon-utils`)\n", - "- rustworkx 0.15 or later (`pip install rustworkx`)" + "- Qiskit addon utils (`pip install qiskit-addon-utils`)" ] }, { diff --git a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb index fd8b3ee9ece..043d59a5429 100644 --- a/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb +++ b/docs/tutorials/pauli-correlation-encoding-for-qaoa.ipynb @@ -9,7 +9,7 @@ "\n", "# Pauli Correlation Encoding to reduce Maxcut requirements\n", "\n", - "*Usage estimate: 30 minutes on ibm_sherbrooke (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 30 minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -60,8 +60,7 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)" + "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)" ] }, { diff --git a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb index 7ebd91970f6..3974963f271 100644 --- a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb +++ b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb @@ -44,8 +44,7 @@ "\n", "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", - "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)" + "- Qiskit Runtime 0.22 or later (`pip install qiskit-ibm-runtime`)" ] }, { @@ -58,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "37b3acfc", "metadata": {}, "outputs": [], @@ -123,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "6ced6bea", "metadata": {}, "outputs": [ @@ -587,17 +586,7 @@ "execution_count": 10, "id": "e14ecc92", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(objective_func_vals)\n", @@ -764,17 +753,7 @@ "execution_count": 15, "id": "33135970-8bc4-4fb2-ab87-08726a432ce4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# auxiliary function to plot graphs\n", "def plot_result(G, x):\n", @@ -801,15 +780,7 @@ "execution_count": 16, "id": "2f6a73c4-f5ae-4647-a0dd-d77a13f66388", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The value of the cut is: 5\n" - ] - } - ], + "outputs": [], "source": [ "def evaluate_sample(x: Sequence[int], graph: rx.PyGraph) -> float:\n", " assert len(x) == len(\n", @@ -840,17 +811,7 @@ "execution_count": 17, "id": "590fe2ce", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "n = 100 # Number of nodes in graph\n", "graph_100 = rx.PyGraph()\n", @@ -1144,17 +1105,7 @@ "execution_count": 24, "id": "0fda3611", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(12, 6))\n", "plt.plot(objective_func_vals)\n", @@ -1245,17 +1196,7 @@ "execution_count": 26, "id": "b4a25e28", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_result(graph_100, best_sol_bitstring_100)" ] @@ -1361,17 +1302,7 @@ "execution_count": 30, "id": "4381a2b3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "plot_cdf(result_dist, ax, \"Eagle device\")" diff --git a/docs/tutorials/quantum-kernel-training.ipynb b/docs/tutorials/quantum-kernel-training.ipynb index b7a219d0cf5..aa861e8eb05 100644 --- a/docs/tutorials/quantum-kernel-training.ipynb +++ b/docs/tutorials/quantum-kernel-training.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Quantum kernel training\n", - "*Usage estimate: under one minute on ibm_nazca (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: under one minute on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { diff --git a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb index 7d61fa6b910..7a4deff0bba 100644 --- a/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb +++ b/docs/tutorials/real-time-benchmarking-for-qubit-selection.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Real-time benchmarking for qubit selection\n", - "*Usage estimate: 4 minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 4 minutes on an Eagle r2 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -30,6 +30,7 @@ "metadata": {}, "source": [ "## Background\n", + "\n", "This tutorial shows how to run real-time characterization experiments and update backend properties to improve qubit selection when mapping a circuit to the physical qubits on a QPU. You will learn the basic characterization experiments that are used to determine properties of the QPU, how to do them in Qiskit, and how to update the properties saved in the backend object representing the QPU based on these experiments.\n", "\n", "The QPU-reported properties are updated once a day, but the system may drift faster than the time between updates. This can affect the reliability of the qubit selection routines in the `Layout` stage of the pass manager, as they'd be using reported properties that don't represent the present state of the QPU. For this reason, it may be worth devoting some QPU time to characterization experiments, which can then be used to update the QPU properties used by the `Layout` routine." @@ -41,6 +42,7 @@ "metadata": {}, "source": [ "## Requirements\n", + "\n", "Before starting this tutorial, be sure you have the following installed:\n", "\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", @@ -198,20 +200,24 @@ "A series of experiments is used to characterize the main properties of the qubits in a QPU. These are $T_1$, $T_2$, readout error, and single-qubit and two-qubit gate error. We'll briefly summarize what these properties are and refer to experiments in the [`qiskit-experiments`](https://qiskit-community.github.io/qiskit-experiments/index.html) package that are used to characterize them.\n", "\n", "#### T1\n", + "\n", "$T_1$ is the characteristic time it takes for an excited qubit to fall to the ground state due to amplitude-damping decoherence processes. In a [$T_1$ experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t1.html), we measure an excited qubit after a delay. The larger the delay time is, the more\n", "likely is the qubit to fall to the ground state. The goal of the\n", "experiment is to characterize the decay rate of the qubit towards the\n", "ground state.\n", "\n", "#### T2\n", + "\n", "$T_2$ represents the amount of time required for a single qubit's Bloch\n", "vector projection on the XY plane to fall to approximately 37% ($\\frac{1}{e}$) of\n", "its initial amplitude due to dephasing decoherence processes. In a [$T_2$ Hahn Echo experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/characterization/t2hahn.html), we can estimate the rate of this decay.\n", "\n", "#### State preparation and measurement (SPAM) error characterization\n", + "\n", "In a [SPAM-error characterization experiment](https://qiskit-community.github.io/qiskit-experiments/manuals/measurement/readout_mitigation.html) qubit are prepared in a certain state ($\\vert 0 \\rangle$ or $\\vert 1 \\rangle$) and measured. The probability of measuring a state different than the one prepared then gives the probability of the error.\n", "\n", "#### Single-qubit and two-qubit randomized benchmarking\n", + "\n", "[Randomized benchmarking](https://qiskit-community.github.io/qiskit-experiments/manuals/verification/randomized_benchmarking.html) is a popular protocol for characterizing the error rate of\n", "quantum processors. An RB experiment consists of the generation of random Clifford\n", "circuits on the given qubits such that the unitary computed by the circuits is the\n", @@ -299,6 +305,7 @@ "metadata": {}, "source": [ "### QPU properties over time\n", + "\n", "Looking at the reported QPU properties over time (we'll consider a single week below), we see how these can fluctuate on a scale of a single day. Small fluctuations can happen even within a day. In this scenario, the reported properties (updated once per day) will not accurately capture the current status of the QPU. Moreover, if a job is transpiled locally (using current reported properties) and submitted but executed only at a later time (minutes or days), it may run the risk of having used outdated properties for qubit selection in the transpilation step. This highlights the importance of having updated information about the QPU at execution time. First, let's retrieve the properties over a certain time range." ] }, @@ -443,6 +450,7 @@ "metadata": {}, "source": [ "## Step 2: Optimize problem for quantum hardware execution\n", + "\n", "No optimization of the circuits or operators is done in this tutorial." ] }, @@ -459,7 +467,8 @@ "id": "d88f925f-3ffd-43de-ac1f-2c5ba3b8cd96", "metadata": {}, "source": [ - "### Execute a quantum circuit with default qubit selection\n", + "### Execute a quantum circuit with default qubit selection\n", + "\n", "As a reference result of performance, we'll execute a quantum circuit on a QPU by using the default qubits, which are the qubits selected with the requested backend properties. We will use `optimization_level = 3`. This setting includes the most advanced transpilation optimization, and uses target properties (like operation errors) to select the best performing qubits for execution." ] }, @@ -634,6 +643,7 @@ "## Step 4: Post-process and return result in desired classical format\n", "\n", "Finally, let's compare the fidelity of the Bell state obtained in the two different settings:\n", + "\n", "- `original`, that is with the default qubits chosen by the transpiler based on reported properties of the backend.\n", "- `updated`, that is with the qubits chosen based on updated properties of the backend after characterization experiments have run." ] @@ -732,9 +742,9 @@ "id": "cd81b208-b13b-4988-854e-1741408f36f3", "metadata": {}, "source": [ - "
\n", - "Call to Action: Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", - "
" + "\n", + "Try to apply this method to your executions and determine how much of a benefit you get! You can also try and see how much improvements you get from different backends.\n", + "" ] }, { diff --git a/docs/tutorials/sample-based-quantum-diagonalization.ipynb b/docs/tutorials/sample-based-quantum-diagonalization.ipynb index 2c5868c991f..477aaaf700a 100644 --- a/docs/tutorials/sample-based-quantum-diagonalization.ipynb +++ b/docs/tutorials/sample-based-quantum-diagonalization.ipynb @@ -432,7 +432,7 @@ " will be mapped and run. This function takes the coupling graph as a undirected\n", " `rustworkx.PyGraph` where there is only one 'undirected' edge between two nodes,\n", " that is, qubits. Usually, the coupling graph of a IBM backend is directed (for example, Eagle devices\n", - " such as ibm_sherbrooke) or may have two edges between two nodes (for example, Heron `ibm_torino`).\n", + " such as ibm_brisbane) or may have two edges between two nodes (for example, Heron `ibm_torino`).\n", " A user needs to be make such graphs undirected and/or remove duplicate edges to make them\n", " compatible with this function.\n", "\n", diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index fa7172c9d59..01de08d28bd 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -8,7 +8,7 @@ }, "source": [ "# Ground-state energy estimation of the Heisenberg chain with VQE\n", - "*Usage estimate: Two minutes on ibm_cusco (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: Two minutes on an Eagle r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -31,7 +31,9 @@ "Before starting this tutorial, ensure that you have the following installed:\n", "\n", "* Qiskit SDK 1.2 or later, with visualization support (`pip install 'qiskit[visualization]'`)\n", - "* Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`) 0.22 or later" + "* Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`) 0.22 or later\n", + "* Qiskit Serverless (pip install qiskit_serverless)\n", + "* IBM Catalog (pip install qiskit-ibm-catalog)" ] }, { @@ -45,7 +47,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e7754922", + "id": "c37bfdb1-bb7c-4177-a3af-37ec32558fd0", "metadata": {}, "outputs": [], "source": [ @@ -71,9 +73,16 @@ "from qiskit_ibm_runtime import QiskitRuntimeService\n", "from qiskit_ibm_runtime import Session, Estimator\n", "\n", - "from qiskit_ibm_catalog import QiskitServerless, QiskitFunction\n", - "\n", - "\n", + "from qiskit_ibm_catalog import QiskitServerless, QiskitFunction" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "faef6e0d-f09d-46fb-8b4f-e1b56f7c6b3a", + "metadata": {}, + "outputs": [], + "source": [ "def visualize_results(results):\n", " plt.plot(results[\"cost_history\"], lw=2)\n", " plt.xlabel(\"Iteration\")\n", @@ -126,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "7e8d2f10-f1d6-4ec2-bac9-9db23499c9e1", "metadata": {}, "outputs": [ @@ -136,7 +145,7 @@ "\"Output" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -235,27 +244,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "4c4b1b0b-5c61-4587-986c-7a9108bc2505", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iters. done: 101 [Current cost: -2.5127326712407005]\r" - ] - }, - { - "data": { - "text/plain": [ - "\"Output" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# SciPy minimizer routine\n", "def cost_func(\n", @@ -281,7 +273,7 @@ " \"cost_history\": [],\n", "}\n", "\n", - "# Evaluate the problem using a QPU via Qiskit IBM Runtime\n", + "# Evaluate the problem on a QPU by using Qiskit IBM Runtime\n", "with Session(backend=backend) as session:\n", " estimator = Estimator()\n", " callback = build_callback(\n", @@ -312,18 +304,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e5b58771-d543-4e75-9746-fbc7b28e4360", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated ground state energy: -2.594437119769288\n" - ] - } - ], + "outputs": [], "source": [ "print(f'Estimated ground state energy: {res[\"fun\"]}')" ] diff --git a/docs/tutorials/transpilation-optimizations-with-sabre.ipynb b/docs/tutorials/transpilation-optimizations-with-sabre.ipynb index fecbcfcb158..6b9b8c62cba 100644 --- a/docs/tutorials/transpilation-optimizations-with-sabre.ipynb +++ b/docs/tutorials/transpilation-optimizations-with-sabre.ipynb @@ -47,7 +47,6 @@ "Before starting this tutorial, be sure you have the following installed:\n", "- Qiskit SDK v1.0 or later, with visualization support ( `pip install 'qiskit[visualization]'` )\n", "- Qiskit Runtime 0.28 or later (`pip install qiskit-ibm-runtime`)\n", - "- Rustworkx graph library (`pip install rustworkx`)\n", "- Serverless (`pip install qiskit-ibm-catalog qiskit_serverless`)" ] }, diff --git a/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb b/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb index 3093042021e..56b7246addc 100644 --- a/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/skqd.ipynb @@ -244,7 +244,7 @@ "service = QiskitRuntimeService()\n", "# Use the least-busy backend or specify a quantum computer using the syntax commented out below.\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyiv\")" + "# backend = service.backend(\"ibm_brisbane\")" ] }, { diff --git a/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb b/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb index bb3a968c60f..16a5bb0b323 100644 --- a/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/sqd-implementation.ipynb @@ -436,7 +436,7 @@ "service = QiskitRuntimeService()\n", "# Use the least-busy backend or specify a quantum computer using the syntax commented out below.\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyiv\")" + "# backend = service.backend(\"ibm_brisbane\")" ] }, { diff --git a/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb b/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb index eeccef218e3..25fe0e68e0a 100644 --- a/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb +++ b/learning/courses/quantum-diagonalization-algorithms/vqe.ipynb @@ -694,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "8e0d33b8-7f9c-428d-a76d-d832747b8430", "metadata": {}, "outputs": [ @@ -745,7 +745,7 @@ } ], "source": [ - "# This required 13 min, 20 s QPU time on ibm_cusco, 28 min total time.\n", + "# This required 13 min, 20 s QPU time on an Eagle processor, 28 min total time.\n", "with Session(backend=backend) as session:\n", " estimator = Estimator(mode=session)\n", " estimator.options.default_shots = 10000\n", diff --git a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb index bf1436c567e..d78580f179a 100644 --- a/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb +++ b/learning/courses/quantum-machine-learning/quantum-kernel-methods.ipynb @@ -214,7 +214,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -320,7 +320,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Benchmarked on ibm_kyoto, 7-11-24, took 4 sec.\n", + "# Benchmarked on an Eagle processor, 7-11-24, took 4 sec.\n", "\n", "# Import our runtime primitive\n", "from qiskit_ibm_runtime import Session, SamplerV2 as Sampler\n", @@ -1069,7 +1069,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], diff --git a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb index ceae86dd135..d7b87334b80 100644 --- a/learning/courses/quantum-machine-learning/qvc-qnn.ipynb +++ b/learning/courses/quantum-machine-learning/qvc-qnn.ipynb @@ -566,7 +566,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "ibm_sherbrooke\n" + "ibm_brisbane\n" ] } ], @@ -1575,7 +1575,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "ibm_nazca\n" + "ibm_brisbane\n" ] } ], @@ -1585,7 +1585,7 @@ "# To run on hardware, select the least busy quantum computer or specify a particular one.\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", - "# backend = service.backend(\"ibm_kyoto\")\n", + "# backend = service.backend(\"ibm_brisbaneane\")\n", "\n", "print(backend.name)" ] @@ -1729,7 +1729,7 @@ "metadata": {}, "outputs": [], "source": [ - "# This was run on ibm_nazca on 10-4-24, and took 7 min.\n", + "# This was run on an Eagle r3 processor on 10-4-24, and took 7 min.\n", "\n", "from qiskit_ibm_runtime import EstimatorV2 as Estimator, Session\n", "\n", diff --git a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb index 3a8c7d30fca..5bde150fd40 100644 --- a/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb +++ b/learning/courses/utility-scale-quantum-computing/quantum-circuit-optimization.ipynb @@ -142,14 +142,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "f6be6161", "metadata": {}, "outputs": [], "source": [ - "from qiskit_ibm_runtime.fake_provider import FakeKyiv\n", + "from qiskit_ibm_runtime.fake_provider import FakeBrisbane\n", "\n", - "backend = FakeKyiv()" + "backend = FakeBrisbane()" ] }, { @@ -500,7 +500,7 @@ "source": [ "The new synthesis produces a shallower circuit. Why?\n", "\n", - "This is because the new circuit can be laid out on linearly connected qubits, so on IBM® Kyiv's heavy-hexagon coupling graph as well, while the original circuit requires star-shaped connectivity (a degree-4 node) and hence cannot be laid out on the heavy-hex coupling graph, which has nodes at most degree 3. As a result, the original circuit requires qubit routing that adds SWAP gates, increasing the gate count.\n", + "This is because the new circuit can be laid out on linearly connected qubits, so on IBM® Brisbane's heavy-hexagon coupling graph as well, while the original circuit requires star-shaped connectivity (a degree-4 node) and hence cannot be laid out on the heavy-hex coupling graph, which has nodes at most degree 3. As a result, the original circuit requires qubit routing that adds SWAP gates, increasing the gate count.\n", "\n", "What we have done in the new circuit can be seen as a manual \"coupling constraint-aware\" circuit synthesis. In other words: manually solving circuit synthesis and circuit mapping at the same time." ] @@ -1178,7 +1178,7 @@ "outputs": [], "source": [ "service = QiskitRuntimeService()\n", - "backend = service.backend(\"ibm_sherbrooke\")\n", + "backend = service.backend(\"ibm_brisbane\")\n", "sampler = Sampler(backend)" ] }, @@ -1265,12 +1265,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "270db9aa", "metadata": {}, "outputs": [], "source": [ - "# backend = service.backend('ibm_sherbrooke')\n", + "# backend = service.backend('ibm_brisbane')\n", "backend = service.least_busy(\n", " operational=True, simulator=False, min_num_qubits=127\n", ") # Eagle\n", diff --git a/learning/courses/utility-scale-quantum-computing/teleportation.ipynb b/learning/courses/utility-scale-quantum-computing/teleportation.ipynb index 8bdc5ba7f42..158f4a93670 100644 --- a/learning/courses/utility-scale-quantum-computing/teleportation.ipynb +++ b/learning/courses/utility-scale-quantum-computing/teleportation.ipynb @@ -1100,7 +1100,6 @@ "data": { "text/plain": [ "[,\n", - " ,\n", " ]" ] }, @@ -1126,7 +1125,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The least busy device is \n" + "The least busy device is \n" ] } ], @@ -1144,7 +1143,7 @@ "outputs": [], "source": [ "# You can specify the device\n", - "# backend = service.backend('ibm_sherbrooke')" + "# backend = service.backend('ibm_brisbane')" ] }, { @@ -1626,7 +1625,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The least busy device is \n" + "The least busy device is \n" ] } ], diff --git a/learning/courses/utility-scale-quantum-computing/utility-i.ipynb b/learning/courses/utility-scale-quantum-computing/utility-i.ipynb index 5f9e8507052..5fb58e0ae14 100644 --- a/learning/courses/utility-scale-quantum-computing/utility-i.ipynb +++ b/learning/courses/utility-scale-quantum-computing/utility-i.ipynb @@ -803,12 +803,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "8b900d9a-0351-426a-b25b-19e631fd257e", "metadata": {}, "outputs": [], "source": [ - "# backend_map = service.backend(\"ibm_kyiv\")\n", + "# backend_map = service.backend(\"ibm_brisbane\")\n", "backend_map = service.least_busy(operational=True, simulator=False)\n", "\n", "num_steps = 20\n", @@ -831,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "f9fadf76-48e3-43a3-b773-8fa3b5fd5f3d", "metadata": {}, "outputs": [ @@ -845,7 +845,7 @@ } ], "source": [ - "# backend = service.backend(\"ibm_kyiv\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "backend = backend_map\n", "\n", "transpiled_qc = transpile(qc, backend, optimization_level=1, layout_method=\"trivial\")\n", diff --git a/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb b/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb index f09e3c6839e..ba8bb9bbe6e 100644 --- a/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb +++ b/learning/courses/utility-scale-quantum-computing/utility-iii.ipynb @@ -2046,7 +2046,7 @@ "\n", "Build a GHZ circuit for 20 qubits or more so that the measurement result meets the criteria: The fidelity of your GHZ state > 0.5.\n", "\n", - "- You need to use an Eagle device (such as `ibm_kyiv`) and set the shots number as 40,000.\n", + "- You need to use an Eagle device (such as `ibm_brisbane`) and set the shots number as 40,000.\n", "- You should execute the GHZ circuit using the `execute_ghz_fidelity` function, and calculate the fidelity using the `check_ghz_fidelity_from_jobs` function.\n", "You need to find the biggest qubits - GHZ circuit which meet the criteria. Write your code below, show the result with the function `check_ghz_fidelity_from_jobs` .\n", "\n", diff --git a/learning/courses/variational-algorithm-design/cost-functions.ipynb b/learning/courses/variational-algorithm-design/cost-functions.ipynb index e3e9d51ae9a..a30d1466447 100644 --- a/learning/courses/variational-algorithm-design/cost-functions.ipynb +++ b/learning/courses/variational-algorithm-design/cost-functions.ipynb @@ -678,7 +678,7 @@ " operational=True, min_num_qubits=ansatz.num_qubits, simulator=False\n", ")\n", "# Or get a specific backend:\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Use a pass manager to transpile the circuit and observable for the specific backend being used:\n", "\n", @@ -950,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "389b7e86-cd61-4184-96c3-ae91b8ad59f5", "metadata": {}, "outputs": [ @@ -976,7 +976,7 @@ ")\n", "\n", "# Or get a specific backend:\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Use a pass manager to transpile the circuit and observable for the specific backend being used:\n", "\n", @@ -1119,12 +1119,12 @@ "id": "ca040383-9c8c-4a38-8e40-bad1b26c6085", "metadata": {}, "source": [ - "We can use a simulator to show the usefulness of an optimized transpilation. We will return below to using real hardware to demonstrate the usefulness of error mitigation. We will use QiskitRuntimeService to get a real backend (in this case, ibm_sherbrooke), and use AerSimulator to simulate that backend, including its noise behavior." + "We can use a simulator to show the usefulness of an optimized transpilation. We will return below to using real hardware to demonstrate the usefulness of error mitigation. We will use QiskitRuntimeService to get a real backend (in this case, ibm_brisbane), and use AerSimulator to simulate that backend, including its noise behavior." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "6f0e6f32-02f7-471f-a9f3-f30d22f8cca4", "metadata": {}, "outputs": [], @@ -1134,7 +1134,7 @@ "\n", "# get a real backend from the runtime service\n", "service = QiskitRuntimeService()\n", - "backend = service.backend(\"ibm_sherbrooke\")\n", + "backend = service.backend(\"ibm_brisbane\")\n", "\n", "# generate a simulator that mimics the real quantum system with the latest calibration results\n", "backend_sim = AerSimulator.from_backend(backend)" @@ -1328,7 +1328,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated usage: 8 minutes, benchmarked on ibm_sherbrooke, 5-23-24\n", + "# Estimated usage: 8 minutes, benchmarked on an Eagle processor, 5-23-24\n", "\n", "from qiskit_ibm_runtime import QiskitRuntimeService\n", "from qiskit_ibm_runtime import (\n", @@ -1345,7 +1345,7 @@ "# )\n", "\n", "# Or use a specific backend\n", - "backend = service.backend(\"ibm_cusco\")\n", + "backend = service.backend(\"ibm_brisbane\")\n", "\n", "# Initialize some variables to save the results from different runs:\n", "\n", diff --git a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb index 3a200617258..536d69205f0 100644 --- a/learning/courses/variational-algorithm-design/examples-and-applications.ipynb +++ b/learning/courses/variational-algorithm-design/examples-and-applications.ipynb @@ -1290,7 +1290,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], @@ -1304,7 +1304,7 @@ "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", "# Or use a specific backend\n", - "# backend = service.backend(\"ibm_cusco\")\n", + "# backend = service.backend(\"ibm_brisbane\")\n", "print(backend)" ] }, @@ -1345,7 +1345,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 s on ibm_nazca on 5-30-24\n", + "# Estimated compute resource usage: 25 minutes. Benchmarked at 24 min, 30 sec on an Eagle r3 processor on 5-30-24\n", "\n", "k = 2\n", "betas = [30, 50, 80]\n", @@ -1788,7 +1788,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Estimated hardware usage: 20 min benchmarked on ibm_nazca on 5-30-24\n", + "# Estimated hardware usage: 20 min benchmarked on an Eagle r3 processor on 5-30-24\n", "\n", "real_prev_states = []\n", "real_prev_opt_parameters = []\n", diff --git a/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb b/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb index dcadc129105..9aa09879796 100644 --- a/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb +++ b/learning/courses/variational-algorithm-design/instances-and-extensions.ipynb @@ -220,7 +220,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], diff --git a/learning/modules/computer-science/quantum-key-distribution.ipynb b/learning/modules/computer-science/quantum-key-distribution.ipynb index 798972991d8..f8706245f33 100644 --- a/learning/modules/computer-science/quantum-key-distribution.ipynb +++ b/learning/modules/computer-science/quantum-key-distribution.ipynb @@ -986,7 +986,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on ibm_sherbrooke on 11-7-24 and required 3 s to run, with 127 qubits.\n", + "# This calculation was run on an Eagle r3 processor on 11-7-24 and required 3 sec to run, with 127 qubits.\n", "# Qiskit patterns step 1: Mapping your problem to a quantum circuit\n", "\n", "bit_num = 127\n", @@ -1095,7 +1095,7 @@ "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", - "# This calculation was run on ibm_nazca on 11-7-24 and required 2 s to run, with 127 qubits.\n", + "# This calculation was run on an Eagle r3 processor on 11-7-24 and required 2 s to run, with 127 qubits.\n", "# Qiskit patterns step 1: 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