Aer - high performance quantum circuit simulation for Qiskit
Aer is a high performance simulator for quantum circuits written in Qiskit, that includes realistic noise models.
| ❗ Reduced Maintenance Mode |
|---|
| Aer is currently operating under reduced maintenance. The maintainers are only able to address critical bug fixes. Feature requests and non-critical enhancements have been placed in the backlog and will be revisited as capacity allows. |
Installation
We encourage installing Aer via the pip tool (a python package manager):
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.
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.
If you want to install our GPU supported simulators, you have to install this other package:
pip install qiskit-aer-gpu
The package above is for CUDA® 12, so if your system has CUDA® 11 installed, install separate package:
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 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
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()
backend = provider.least_busy()
# 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=backend.basis_gates), 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. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. Please use our slack for discussion and simple questions. To join our Slack community use the link or submit a request to join the Qiskit slack workspace. For questions that are more suited for a forum, we use the Qiskit tag in the Stack Exchange.
Next Steps
Now you're set up and ready to check out some of the other examples from the Aer documentation.
Authors and Citation
Aer is the work of many people who contribute to the project at different levels. If you use Qiskit, please cite as per the included BibTeX file.
License
Metadata
Release files for qiskit-aer 0.18.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distributions (wheels)
Total release size: 280.1 MB
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