diff --git a/tutorials/repeat-until-success/notebook.ipynb b/tutorials/repeat-until-success/notebook.ipynb index 3bdfce3953d..f488658b532 100644 --- a/tutorials/repeat-until-success/notebook.ipynb +++ b/tutorials/repeat-until-success/notebook.ipynb @@ -12,9 +12,9 @@ "\n", "## Background\n", "\n", - "This tutorial demonstrates the current capabilities of IBM dynamic-circuit backends to use mid-circuit measurements to produce a circuit that repeatedly attempts its setup until a syndrome measurement reveals that it has been successful.\n", + "This tutorial demonstrates how certain IBM Quantum™ systems (those that support dynamic circuits) use mid-circuit measurements to produce a circuit that repeatedly attempts its setup until a syndrome measurement reveals that it has been successful.\n", "\n", - "We will build an abstract circuit that uses the non-parametrized gate set $\\{H,\\,X,\\,S,\\,\\text{Toffoli}\\}$ to construct a heralded $R_X(\\theta)$ gate on a target qubit, where $\\theta$ satisfies $\\cos\\theta = \\frac35$. Each iteration of the circuit only has a finite chance of success, but successes are heralded, so we will use our dynamic-circuit capabilities to repeat the setup until it succeeds." + "Build an abstract circuit that uses the non-parametrized gate set $\\{H,\\,X,\\,S,\\,\\text{Toffoli}\\}$ to construct a heralded $R_X(\\theta)$ gate on a target qubit, where $\\theta$ satisfies $\\cos\\theta = \\frac35$. Each iteration of the circuit has a finite chance of success, but because successes are heralded, dynamic circuit capabilities are used to repeat the setup until it succeeds." ] }, { @@ -34,36 +34,10 @@ "source": [ "# Qiskit imports\n", "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n", - "from qiskit import transpile\n", "\n", - "# Qiskit Aer (Simulator)\n", - "from qiskit_aer import AerSimulator\n", - "\n", - "# Qiskit IBM Provider\n", - "from qiskit_ibm_provider import IBMProvider, least_busy" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "97a795e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "provider = IBMProvider()\n", - "backend = least_busy(provider.backends(simulator=False, operational=True))\n", - "backend" + "# Qiskit Runtime\n", + "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", + "from qiskit_ibm_runtime import QiskitRuntimeService" ] }, { @@ -85,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "a86e5455", "metadata": {}, "outputs": [], @@ -101,18 +75,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "c40b6af4-ae9c-49c1-b03a-78ef374c7280", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/svg+xml": [ + "" + ], "text/plain": [ "
" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -140,7 +116,7 @@ "id": "87e96cf7-68b9-4822-a559-85cfa01eef90", "metadata": {}, "source": [ - "If _both_ measurements of the control bits return $0$, the applied gate is $R_X(\\theta)$ with $\\cos\\theta = \\frac35$. If any of the measurements are $1$, then the applied gate is simply $X$, which is a failure. This is our heralding; we can tell from the measurement whether we applied the correct gate, without disturbing the coherence of the target qubit. Without fully reproducing the mathematics, the success probability of this gate is:\n", + "If _both_ control bit measurements return $0$, the gate $R_X(\\theta)$ with $\\cos\\theta = \\frac35$ is applied. If any of the measurements are $1$, gate $X$ is applied, which is a failure. This is the heralding; you can tell from the measurement whether the correct gate was applied, without disturbing the coherence of the target qubit. Without fully reproducing the mathematics, the success probability of this gate is:\n", "$$\n", "P_{\\text{success}} = \\frac{\n", "{|3 + i|}^2\n", @@ -151,12 +127,12 @@ "= \\frac58\n", "$$\n", "\n", - "If there is a failure, we should reset the dirty state, and start again. Since we know what is applied in the case of a failure, we can use this knowledge to perform the reset efficiently, without using a general hardware reset. For the two auxiliary qubits, this is just an $X$ gate conditioned on its respective measurement being $1$. IBM hardware has a special fast-path for when qubits are conditioned on the result of their own measurement, so this is more efficient than most control flow." + "If there is a failure, reset the \"dirty\" state and start again. Because you know what is applied in the case of a failure, you can use this knowledge to perform the reset efficiently without using a general hardware reset. For the two auxiliary qubits, this is an $X$ gate conditioned on its respective measurement being $1$. IBM® hardware has a special fast path for when qubits are conditioned on the result of their own measurement, so this is more efficient than most control flows." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "4d5ec413-0c18-4684-b29e-d2f60ee941be", "metadata": {}, "outputs": [], @@ -175,23 +151,25 @@ "id": "4f6d46fe-5eb7-4e57-83c0-a8c13ab4d67a", "metadata": {}, "source": [ - "Qiskit cannot directly represent an _inequality_ condition, which is what we require. We only need to do the repeat if the mid-circuit measurement result was not the bitstring `\"00\"`. Instead, we can create an `if` statement with the condition `mid_measure == \"00\"`, pass an empty block, and then use the `else` branch to perform the logic we want." + "Qiskit cannot directly represent an inequality condition, which is required for this exercise. You only need to repeat if the mid-circuit measurement result was not the bitstring `\"00\"`. Instead, you can create an `if` statement with the condition `mid_measure == \"00\"`, pass an empty block, and then use the `else` branch to perform the necessary logic." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "bf4a7dd4-f24b-4612-915e-771ac02b37cb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/svg+xml": [ + "" + ], "text/plain": [ "
" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -199,8 +177,8 @@ "source": [ "max_trials = 2\n", "\n", - "# Manually add the rest of the trials. In the future, we will be\n", - "# able to use a dynamic `while` loop to do this, but for now, we\n", + "# Manually add the rest of the trials. In the future, you can\n", + "# use a dynamic `while` loop to do this, but for now,\n", "# statically add each loop iteration with a manual condition check\n", "# on each one. This involves more classical synchronizations than\n", "# the while loop, but will suffice for now.\n", @@ -217,12 +195,12 @@ " # Then repeat the trial.\n", " trial(circuit, target, controls, mid_measure)\n", "\n", - "# We need to measure the control qubits again to ensure we\n", + "# Measure the control qubits again to ensure you\n", "# get their final results; this is a hardware limitation.\n", "circuit.measure(controls, mid_measure)\n", "\n", - "# Finally, let's measure our target, to check that we're\n", - "# getting the rotation we desired.\n", + "# Finally, measure the target, to check that you're\n", + "# getting the desired rotation.\n", "circuit.measure(target, final_measure)\n", "\n", "circuit.draw(output=\"mpl\", style='iqp', cregbundle=False)" @@ -234,29 +212,43 @@ "id": "e563959a-1543-48b2-ab90-d5dfe8a6ddbc", "metadata": {}, "source": [ - "## Optimize the circuits" + "## Run in local testing mode" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9d88c016-f6e6-498f-854b-3eba2da6d4a2", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n", + "\n", + "# Run the sampler job locally using FakeSherbrooke\n", + "backend = FakeSherbrooke()" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "31be5d78-8115-4a20-9a22-7d99e07ab4dc", + "id": "0029abf2", "metadata": {}, "source": [ - "To prepare the circuit to run on real hardware, we use Qiskit's `transpile` function. By not specifying an `optimization_level` argument to `transpile`, we implicitly use level 1, which has light quantum optimization. For now, levels 2 and 3 do not support the dynamic circuits path as some of the optimizations used at those levels are not fully control-flow-aware." + "### Convert to ISA input" ] }, { "cell_type": "code", "execution_count": 7, - "id": "9d88c016-f6e6-498f-854b-3eba2da6d4a2", + "id": "541d3ec7-277f-482a-be5a-b350f87aba60", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/svg+xml": [ + "" + ], "text/plain": [ - "
" + "
" ] }, "execution_count": 7, @@ -265,108 +257,193 @@ } ], "source": [ - "built = transpile(circuit, backend)\n", - "built.draw(output=\"mpl\", style='iqp', idle_wires=False, cregbundle=False)" + "# Circuits must obey the Instruction Set Architecture (ISA) of a particular backend.\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", + "isa_circuit = pm.run(circuit)\n", + "\n", + "isa_circuit.draw(output=\"mpl\", style='iqp', idle_wires=False, cregbundle=False)" ] }, { "cell_type": "markdown", - "id": "0029abf2", + "id": "998d0f78", "metadata": {}, "source": [ - "## Run on a simulator" + "### Run the job" ] }, { "cell_type": "code", "execution_count": 8, - "id": "541d3ec7-277f-482a-be5a-b350f87aba60", + "id": "7287136b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Simulator counts for mid: {'00': 804, '10': 71, '01': 63, '11': 86}\n", + ">>> Simulator counts for final: {'0': 696, '1': 328}\n" + ] + } + ], + "source": [ + "sampler = Sampler(backend)\n", + "job = sampler.run([isa_circuit])\n", + "\n", + "simulator_counts = job.result()[0]\n", + "print(f\">>> Simulator counts for mid: {simulator_counts.data.mid.get_counts()}\")\n", + "print(f\">>> Simulator counts for final: {simulator_counts.data.final.get_counts()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "5db4e7c3-a081-43bd-93a9-6b16c9506c7f", + "metadata": {}, + "source": [ + "## Run on IBM hardware" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b8ad7e40-fd87-49e8-9c21-6ef285adea79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Connected to ibm_kyiv backend.\n" + ] + } + ], + "source": [ + "service = QiskitRuntimeService()\n", + "backend = service.least_busy(operational=True, simulator=False)\n", + "print(f\">>> Connected to {backend.name} backend.\")" + ] + }, + { + "cell_type": "markdown", + "id": "47d3c339", + "metadata": {}, + "source": [ + "### Convert to ISA input" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "15956b9b", "metadata": {}, "outputs": [ { "data": { + "image/svg+xml": [ + "" + ], "text/plain": [ - "{'1 11': 94,\n", - " '0 11': 89,\n", - " '0 01': 79,\n", - " '1 00': 224,\n", - " '1 10': 90,\n", - " '1 01': 115,\n", - " '0 10': 109,\n", - " '0 00': 224}" + "
" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sim = AerSimulator().from_backend(backend)\n", - "simulator_result = sim.run(built).result()\n", - "simulator_counts = simulator_result.get_counts()\n", - "simulator_counts" + "# Circuits must obey the ISA of a particular backend.\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "\n", + "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", + "isa_circuit = pm.run(circuit)\n", + "\n", + "isa_circuit.draw(output=\"mpl\", style='iqp', idle_wires=False, cregbundle=False)" ] }, { "cell_type": "markdown", - "id": "5db4e7c3-a081-43bd-93a9-6b16c9506c7f", + "id": "296e450e", "metadata": {}, "source": [ - "## Run on IBM hardware" + "### Run the job" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "b8ad7e40-fd87-49e8-9c21-6ef285adea79", + "execution_count": 11, + "id": "5217697f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'cq58wbh7z1hg008fjfh0'" + "'crh9kwzx30tg008kmm4g'" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "job = backend.run(built, dynamic=True)\n", + "sampler = Sampler(backend)\n", + "job = sampler.run([isa_circuit])\n", "job.job_id()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "313c7ef1-75b6-4ee5-8cc0-fd760c177329", "metadata": { "tags": [] }, "outputs": [ { - "data": { - "text/plain": [ - "{'0 00': 1580,\n", - " '0 01': 226,\n", - " '0 10': 198,\n", - " '0 11': 339,\n", - " '1 00': 995,\n", - " '1 01': 228,\n", - " '1 10': 156,\n", - " '1 11': 278}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Hardware counts for mid: {'00': 3165, '01': 322, '10': 296, '11': 313}\n", + ">>> Hardware counts for final: {'1': 1505, '0': 2591}\n" + ] } ], "source": [ - "hardware_counts = job.result().get_counts()\n", - "hardware_counts" + "hardware_counts = job.result()[0]\n", + "\n", + "data_mid = job.result()[0].data.mid\n", + "data_final = job.result()[0].data.final\n", + "\n", + "print(f\">>> Hardware counts for mid: {data_mid.get_counts()}\")\n", + "print(f\">>> Hardware counts for final: {data_final.get_counts()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0529dc0b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Hardware merged data: {'00-1': 993, '01-0': 147, '00-0': 2172, '10-0': 127, '10-1': 169, '11-0': 145, '11-1': 168, '01-1': 175}\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "\n", + "merged_data = defaultdict(int)\n", + "for bs1, bs2 in zip(data_mid.get_bitstrings(), data_final.get_bitstrings()):\n", + " merged_data[f\"{bs1}-{bs2}\"] += 1\n", + "\n", + "print(f\">>> Hardware merged data: {dict(merged_data)}\")" ] }, { @@ -383,38 +460,36 @@ "id": "f63d48c1-6f79-4491-bb45-36e57595b967", "metadata": {}, "source": [ - "A successful result is one in which the measurements on the two controls end in the `00` state. Let's filter those out, and see how many successes will result. This is a type of post-selection. In the complete repeat-until-success circuit, with a dynamic `while` loop, we would not need this, as we would be guaranteed a success. However, in the interim, we can use the probabilities to examine the quality of the output, and verify that the probabilities are what we expect." + "A successful result is one in which the measurements on the two controls end in the `00` state. You can filter those out and see how many successes will result. This is a type of post-selection. In the complete repeat-until-success circuit with a dynamic `while` loop, this would not be necessary, as you would be guaranteed a success. However, in the interim, you can use the probabilities to examine the quality of the output and verify that the probabilities are as expected." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "4df57085-ccfe-4b4a-89fc-e8ceabb3e304", "metadata": {}, "outputs": [], "source": [ - "import collections\n", - "\n", "def marginalize_successes(counts):\n", " \"\"\"Split the full output `counts` dictionary\n", " into two separate dictionaries, marginalizing\n", " the results to leave only the target qubit's\n", " state.\"\"\"\n", - " successes = collections.defaultdict(int)\n", - " failures = collections.defaultdict(int)\n", + " successes = defaultdict(int)\n", + " failures = defaultdict(int)\n", "\n", " for key, value in counts.items():\n", - " if key.endswith(\"00\"):\n", - " successes[key[0]] += value\n", + " if key.startswith(\"00\"):\n", + " successes[key[-1]] += value\n", " else:\n", - " failures[key[0]] += value\n", + " failures[key[-1]] += value\n", "\n", " return successes, failures" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "ea866662-c967-4ed6-a10b-952ec0f1b09d", "metadata": {}, "outputs": [ @@ -422,15 +497,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Expected success rate 0.859. Actual rate 0.644.\n" + "Expected success rate 0.859. Actual rate 0.773.\n" ] } ], "source": [ - "hw_successes, hw_failures = marginalize_successes(hardware_counts)\n", + "hw_successes, hw_failures = marginalize_successes(merged_data)\n", "\n", "expected_successes = 1 - (1 - 5/8)**max_trials\n", - "actual_successes = sum(hw_successes.values()) / sum(hardware_counts.values())\n", + "actual_successes = sum(hw_successes.values()) / sum(merged_data.values())\n", "print(f\"Expected success rate {expected_successes:5.3f}. Actual rate {actual_successes:5.3f}.\")" ] }, @@ -445,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "ab1bfa7d-3574-4f77-8720-17ef009efabd", "metadata": {}, "outputs": [ @@ -453,7 +528,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Expected |1> probability: 0.2. Actual probability 0.386.\n" + "Expected |1> probability: 0.2. Actual probability 0.314.\n" ] } ], @@ -464,40 +539,40 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "c8cfa9c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.9.0'" + "'0.22.0'" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import qiskit_ibm_provider\n", + "import qiskit_ibm_runtime\n", "\n", - "qiskit_ibm_provider.version.get_version_info()" + "qiskit_ibm_runtime.version.get_version_info()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "id": "226adfa2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.0.0rc1'" + "'1.0.2'" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }