mirror of https://github.com/Qiskit/qiskit-aer.git
165 lines
6.6 KiB
Markdown
Executable File
165 lines
6.6 KiB
Markdown
Executable File
# Aer - high performance quantum circuit simulation for Qiskit
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://github.com/Qiskit/qiskit-aer/actions/workflows/build.yml)
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[](https://github.com/Qiskit/qiskit-aer/actions/workflows/tests.yml)
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[](https://github.com/Qiskit/qiskit-aer/releases)
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[](https://pypi.org/project/qiskit-aer/)
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**Aer** is a high performance simulator for quantum circuits written in Qiskit, that includes realistic noise models.
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## Installation
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We encourage installing Aer via the pip tool (a python package manager):
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```bash
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pip install qiskit-aer
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```
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Pip will handle all dependencies automatically for us, and you will always install the latest (and well-tested) version.
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To install from source, follow the instructions in the [contribution guidelines](CONTRIBUTING.md).
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## Installing GPU support
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In order to install and run the GPU supported simulators on Linux, you need CUDA® 11.2 or newer previously installed.
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CUDA® itself would require a set of specific GPU drivers. Please follow CUDA® installation procedure in the NVIDIA® [web](https://www.nvidia.com/drivers).
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If you want to install our GPU supported simulators, you have to install this other package:
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```bash
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pip install qiskit-aer-gpu
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```
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The package above is for CUDA® 12, so if your system has CUDA® 11 installed, install separate package:
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```bash
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pip install qiskit-aer-gpu-cu11
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```
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This will overwrite your current `qiskit-aer` package installation giving you
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the same functionality found in the canonical `qiskit-aer` package, plus the
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ability to run the GPU supported simulators: statevector, density matrix, and unitary.
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**Note**: This package is only available on x86_64 Linux. For other platforms
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that have CUDA support, you will have to build from source. You can refer to
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the [contributing guide](CONTRIBUTING.md#building-with-gpu-support)
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for instructions on doing this.
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## Simulating your first Qiskit circuit with Aer
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Now that you have Aer installed, you can start simulating quantum circuits using primitives and noise models. Here is a basic example:
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```
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$ python
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```
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```python
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from qiskit import transpile
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from qiskit.circuit.library import RealAmplitudes
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from qiskit.quantum_info import SparsePauliOp
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from qiskit_aer import AerSimulator
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sim = AerSimulator()
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# --------------------------
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# Simulating using estimator
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#---------------------------
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from qiskit_aer.primitives import EstimatorV2
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psi1 = transpile(RealAmplitudes(num_qubits=2, reps=2), sim, optimization_level=0)
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psi2 = transpile(RealAmplitudes(num_qubits=2, reps=3), sim, optimization_level=0)
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H1 = SparsePauliOp.from_list([("II", 1), ("IZ", 2), ("XI", 3)])
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H2 = SparsePauliOp.from_list([("IZ", 1)])
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H3 = SparsePauliOp.from_list([("ZI", 1), ("ZZ", 1)])
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theta1 = [0, 1, 1, 2, 3, 5]
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theta2 = [0, 1, 1, 2, 3, 5, 8, 13]
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theta3 = [1, 2, 3, 4, 5, 6]
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estimator = EstimatorV2()
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# calculate [ [<psi1(theta1)|H1|psi1(theta1)>,
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# <psi1(theta3)|H3|psi1(theta3)>],
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# [<psi2(theta2)|H2|psi2(theta2)>] ]
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job = estimator.run(
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[
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(psi1, [H1, H3], [theta1, theta3]),
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(psi2, H2, theta2)
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],
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precision=0.01
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)
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result = job.result()
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print(f"expectation values : psi1 = {result[0].data.evs}, psi2 = {result[1].data.evs}")
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# --------------------------
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# Simulating using sampler
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# --------------------------
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from qiskit_aer.primitives import SamplerV2
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from qiskit import QuantumCircuit
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# create a Bell circuit
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bell = QuantumCircuit(2)
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bell.h(0)
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bell.cx(0, 1)
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bell.measure_all()
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# create two parameterized circuits
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pqc = RealAmplitudes(num_qubits=2, reps=2)
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pqc.measure_all()
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pqc = transpile(pqc, sim, optimization_level=0)
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pqc2 = RealAmplitudes(num_qubits=2, reps=3)
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pqc2.measure_all()
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pqc2 = transpile(pqc2, sim, optimization_level=0)
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theta1 = [0, 1, 1, 2, 3, 5]
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theta2 = [0, 1, 2, 3, 4, 5, 6, 7]
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# initialization of the sampler
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sampler = SamplerV2()
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# collect 128 shots from the Bell circuit
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job = sampler.run([bell], shots=128)
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job_result = job.result()
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print(f"counts for Bell circuit : {job_result[0].data.meas.get_counts()}")
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# run a sampler job on the parameterized circuits
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job2 = sampler.run([(pqc, theta1), (pqc2, theta2)])
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job_result = job2.result()
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print(f"counts for parameterized circuit : {job_result[0].data.meas.get_counts()}")
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# --------------------------------------------------
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# Simulating with noise model from actual hardware
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# --------------------------------------------------
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from qiskit_ibm_runtime import QiskitRuntimeService
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provider = QiskitRuntimeService(channel='ibm_quantum', token="set your own token here")
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backend = provider.get_backend("ibm_kyoto")
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# create sampler from the actual backend
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sampler = SamplerV2.from_backend(backend)
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# run a sampler job on the parameterized circuits with noise model of the actual hardware
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bell_t = transpile(bell, AerSimulator(basis_gates=["ecr", "id", "rz", "sx"]), optimization_level=0)
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job3 = sampler.run([bell_t], shots=128)
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job_result = job3.result()
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print(f"counts for Bell circuit w/noise: {job_result[0].data.meas.get_counts()}")
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```
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## Contribution Guidelines
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If you'd like to contribute to Aer, please take a look at our
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[contribution guidelines](CONTRIBUTING.md). This project adheres to Qiskit's [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.
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We use [GitHub issues](https://github.com/Qiskit/qiskit-aer/issues) for tracking requests and bugs. Please use our [slack](https://qiskit.slack.com) for discussion and simple questions. To join our Slack community use the [link](https://qiskit.slack.com/join/shared_invite/zt-fybmq791-hYRopcSH6YetxycNPXgv~A#/). For questions that are more suited for a forum, we use the Qiskit tag in the [Stack Exchange](https://quantumcomputing.stackexchange.com/questions/tagged/qiskit).
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## Next Steps
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Now you're set up and ready to check out some of the other examples from the [Aer documentation](https://qiskit.github.io/qiskit-aer/).
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## Authors and Citation
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Aer is the work of [many people](https://github.com/Qiskit/qiskit-aer/graphs/contributors) who contribute to the project at different levels.
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If you use Qiskit, please cite as per the included [BibTeX file](https://github.com/Qiskit/qiskit/blob/main/CITATION.bib).
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## License
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[Apache License 2.0](LICENSE.txt)
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