# Aer - high performance quantum circuit simulation for Qiskit
[](https://opensource.org/licenses/Apache-2.0)
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[](https://github.com/Qiskit/qiskit-aer/actions/workflows/tests.yml)
[](https://github.com/Qiskit/qiskit-aer/releases)
[](https://pypi.org/project/qiskit-aer/)
**Aer** is a high performance simulator for quantum circuits written in Qiskit, that includes realistic noise models.
## Installation
We encourage installing Aer via the pip tool (a python package manager):
```bash
pip install qiskit-aer
```
Pip will handle all dependencies automatically for us, and you will always install the latest (and well-tested) version.
To install from source, follow the instructions in the [contribution guidelines](CONTRIBUTING.md).
## Installing GPU support
In order to install and run the GPU supported simulators on Linux, you need CUDA® 11.2 or newer previously installed.
CUDA® itself would require a set of specific GPU drivers. Please follow CUDA® installation procedure in the NVIDIA® [web](https://www.nvidia.com/drivers).
If you want to install our GPU supported simulators, you have to install this other package:
```bash
pip install qiskit-aer-gpu
```
The package above is for CUDA® 12, so if your system has CUDA® 11 installed, install separate package:
```bash
pip install qiskit-aer-gpu-cu11
```
This will overwrite your current `qiskit-aer` package installation giving you
the same functionality found in the canonical `qiskit-aer` package, plus the
ability to run the GPU supported simulators: statevector, density matrix, and unitary.
**Note**: This package is only available on x86_64 Linux. For other platforms
that have CUDA support, you will have to build from source. You can refer to
the [contributing guide](CONTRIBUTING.md#building-with-gpu-support)
for instructions on doing this.
## Simulating your first Qiskit circuit with Aer
Now that you have Aer installed, you can start simulating quantum circuits using primitives and noise models. Here is a basic example:
```
$ python
```
```python
from qiskit import transpile
from qiskit.circuit.library import RealAmplitudes
from qiskit.quantum_info import SparsePauliOp
from qiskit_aer import AerSimulator
sim = AerSimulator()
# --------------------------
# Simulating using estimator
#---------------------------
from qiskit_aer.primitives import EstimatorV2
psi1 = transpile(RealAmplitudes(num_qubits=2, reps=2), sim, optimization_level=0)
psi2 = transpile(RealAmplitudes(num_qubits=2, reps=3), sim, optimization_level=0)
H1 = SparsePauliOp.from_list([("II", 1), ("IZ", 2), ("XI", 3)])
H2 = SparsePauliOp.from_list([("IZ", 1)])
H3 = SparsePauliOp.from_list([("ZI", 1), ("ZZ", 1)])
theta1 = [0, 1, 1, 2, 3, 5]
theta2 = [0, 1, 1, 2, 3, 5, 8, 13]
theta3 = [1, 2, 3, 4, 5, 6]
estimator = EstimatorV2()
# calculate [ [<psi1(theta1)|H1|psi1(theta1)>,
# <psi1(theta3)|H3|psi1(theta3)>],
# [<psi2(theta2)|H2|psi2(theta2)>] ]
job = estimator.run(
[
(psi1, [H1, H3], [theta1, theta3]),
(psi2, H2, theta2)
],
precision=0.01
)
result = job.result()
print(f"expectation values : psi1 = {result[0].data.evs}, psi2 = {result[1].data.evs}")
# --------------------------
# Simulating using sampler
# --------------------------
from qiskit_aer.primitives import SamplerV2
from qiskit import QuantumCircuit
# create a Bell circuit
bell = QuantumCircuit(2)
bell.h(0)
bell.cx(0, 1)
bell.measure_all()
# create two parameterized circuits
pqc = RealAmplitudes(num_qubits=2, reps=2)
pqc.measure_all()
pqc = transpile(pqc, sim, optimization_level=0)
pqc2 = RealAmplitudes(num_qubits=2, reps=3)
pqc2.measure_all()
pqc2 = transpile(pqc2, sim, optimization_level=0)
theta1 = [0, 1, 1, 2, 3, 5]
theta2 = [0, 1, 2, 3, 4, 5, 6, 7]
# initialization of the sampler
sampler = SamplerV2()
# collect 128 shots from the Bell circuit
job = sampler.run([bell], shots=128)
job_result = job.result()
print(f"counts for Bell circuit : {job_result[0].data.meas.get_counts()}")
# run a sampler job on the parameterized circuits
job2 = sampler.run([(pqc, theta1), (pqc2, theta2)])
job_result = job2.result()
print(f"counts for parameterized circuit : {job_result[0].data.meas.get_counts()}")
# --------------------------------------------------
# Simulating with noise model from actual hardware
# --------------------------------------------------
from qiskit_ibm_runtime import QiskitRuntimeService
provider = QiskitRuntimeService(channel='ibm_quantum', token="set your own token here")
backend = provider.get_backend("ibm_kyoto")
# create sampler from the actual backend
sampler = SamplerV2.from_backend(backend)
# run a sampler job on the parameterized circuits with noise model of the actual hardware
bell_t = transpile(bell, AerSimulator(basis_gates=["ecr", "id", "rz", "sx"]), optimization_level=0)
job3 = sampler.run([bell_t], shots=128)
job_result = job3.result()
print(f"counts for Bell circuit w/noise: {job_result[0].data.meas.get_counts()}")
```
## Contribution Guidelines
If you'd like to contribute to Aer, please take a look at our
[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.
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).
## Next Steps
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/).
## Authors and Citation
Aer is the work of [many people](https://github.com/Qiskit/qiskit-aer/graphs/contributors) who contribute to the project at different levels.
If you use Qiskit, please cite as per the included [BibTeX file](https://github.com/Qiskit/qiskit/blob/main/CITATION.bib).
## License
[Apache License 2.0](LICENSE.txt)
Raw data
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"description": "# Aer - high performance quantum circuit simulation for Qiskit\n\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://github.com/Qiskit/qiskit-aer/actions/workflows/build.yml)\n[](https://github.com/Qiskit/qiskit-aer/actions/workflows/tests.yml)\n[](https://github.com/Qiskit/qiskit-aer/releases)\n[](https://pypi.org/project/qiskit-aer/)\n\n**Aer** is a high performance simulator for quantum circuits written in Qiskit, that includes realistic noise models.\n\n## Installation\n\nWe encourage installing Aer via the pip tool (a python package manager):\n\n```bash\npip install qiskit-aer\n```\n\nPip will handle all dependencies automatically for us, and you will always install the latest (and well-tested) version.\n\nTo install from source, follow the instructions in the [contribution guidelines](CONTRIBUTING.md).\n\n## Installing GPU support\n\nIn order to install and run the GPU supported simulators on Linux, you need CUDA® 11.2 or newer previously installed.\nCUDA® itself would require a set of specific GPU drivers. Please follow CUDA® installation procedure in the NVIDIA® [web](https://www.nvidia.com/drivers).\n\nIf you want to install our GPU supported simulators, you have to install this other package:\n\n```bash\npip install qiskit-aer-gpu\n```\n\nThe package above is for CUDA® 12, so if your system has CUDA® 11 installed, install separate package:\n```bash\npip install qiskit-aer-gpu-cu11\n```\n\nThis will overwrite your current `qiskit-aer` package installation giving you\nthe same functionality found in the canonical `qiskit-aer` package, plus the\nability to run the GPU supported simulators: statevector, density matrix, and unitary.\n\n**Note**: This package is only available on x86_64 Linux. For other platforms\nthat have CUDA support, you will have to build from source. You can refer to\nthe [contributing guide](CONTRIBUTING.md#building-with-gpu-support)\nfor instructions on doing this.\n\n## Simulating your first Qiskit circuit with Aer\nNow that you have Aer installed, you can start simulating quantum circuits using primitives and noise models. Here is a basic example:\n\n```\n$ python\n```\n\n```python\nfrom qiskit import transpile\nfrom qiskit.circuit.library import RealAmplitudes\nfrom qiskit.quantum_info import SparsePauliOp\nfrom qiskit_aer import AerSimulator\n\nsim = AerSimulator()\n# --------------------------\n# Simulating using estimator\n#---------------------------\nfrom qiskit_aer.primitives import EstimatorV2\n\npsi1 = transpile(RealAmplitudes(num_qubits=2, reps=2), sim, optimization_level=0)\npsi2 = transpile(RealAmplitudes(num_qubits=2, reps=3), sim, optimization_level=0)\n\nH1 = SparsePauliOp.from_list([(\"II\", 1), (\"IZ\", 2), (\"XI\", 3)])\nH2 = SparsePauliOp.from_list([(\"IZ\", 1)])\nH3 = SparsePauliOp.from_list([(\"ZI\", 1), (\"ZZ\", 1)])\n\ntheta1 = [0, 1, 1, 2, 3, 5]\ntheta2 = [0, 1, 1, 2, 3, 5, 8, 13]\ntheta3 = [1, 2, 3, 4, 5, 6]\n\nestimator = EstimatorV2()\n\n# calculate [ [<psi1(theta1)|H1|psi1(theta1)>,\n# <psi1(theta3)|H3|psi1(theta3)>],\n# [<psi2(theta2)|H2|psi2(theta2)>] ]\njob = estimator.run(\n [\n (psi1, [H1, H3], [theta1, theta3]),\n (psi2, H2, theta2)\n ],\n precision=0.01\n)\nresult = job.result()\nprint(f\"expectation values : psi1 = {result[0].data.evs}, psi2 = {result[1].data.evs}\")\n\n# --------------------------\n# Simulating using sampler\n# --------------------------\nfrom qiskit_aer.primitives import SamplerV2\nfrom qiskit import QuantumCircuit\n\n# create a Bell circuit\nbell = QuantumCircuit(2)\nbell.h(0)\nbell.cx(0, 1)\nbell.measure_all()\n\n# create two parameterized circuits\npqc = RealAmplitudes(num_qubits=2, reps=2)\npqc.measure_all()\npqc = transpile(pqc, sim, optimization_level=0)\npqc2 = RealAmplitudes(num_qubits=2, reps=3)\npqc2.measure_all()\npqc2 = transpile(pqc2, sim, optimization_level=0)\n\ntheta1 = [0, 1, 1, 2, 3, 5]\ntheta2 = [0, 1, 2, 3, 4, 5, 6, 7]\n\n# initialization of the sampler\nsampler = SamplerV2()\n\n# collect 128 shots from the Bell circuit\njob = sampler.run([bell], shots=128)\njob_result = job.result()\nprint(f\"counts for Bell circuit : {job_result[0].data.meas.get_counts()}\")\n \n# run a sampler job on the parameterized circuits\njob2 = sampler.run([(pqc, theta1), (pqc2, theta2)])\njob_result = job2.result()\nprint(f\"counts for parameterized circuit : {job_result[0].data.meas.get_counts()}\")\n\n# --------------------------------------------------\n# Simulating with noise model from actual hardware\n# --------------------------------------------------\nfrom qiskit_ibm_runtime import QiskitRuntimeService\nprovider = QiskitRuntimeService(channel='ibm_quantum', token=\"set your own token here\")\nbackend = provider.get_backend(\"ibm_kyoto\")\n\n# create sampler from the actual backend\nsampler = SamplerV2.from_backend(backend)\n\n# run a sampler job on the parameterized circuits with noise model of the actual hardware\nbell_t = transpile(bell, AerSimulator(basis_gates=[\"ecr\", \"id\", \"rz\", \"sx\"]), optimization_level=0)\njob3 = sampler.run([bell_t], shots=128)\njob_result = job3.result()\nprint(f\"counts for Bell circuit w/noise: {job_result[0].data.meas.get_counts()}\")\n```\n\n## Contribution Guidelines\n\nIf you'd like to contribute to Aer, please take a look at our\n[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.\n\nWe 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).\n\n## Next Steps\n\nNow you're set up and ready to check out some of the other examples from the [Aer documentation](https://qiskit.github.io/qiskit-aer/).\n\n## Authors and Citation\n\nAer is the work of [many people](https://github.com/Qiskit/qiskit-aer/graphs/contributors) who contribute to the project at different levels.\nIf you use Qiskit, please cite as per the included [BibTeX file](https://github.com/Qiskit/qiskit/blob/main/CITATION.bib).\n\n## License\n\n[Apache License 2.0](LICENSE.txt)\n",
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