This release is a pre-release and may not be stable for production use.
Qiskit
Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.
This library is the core component of Qiskit, which contains the building blocks for creating and working with quantum circuits, quantum operators, and primitive functions (Sampler and Estimator). It also contains a transpiler that supports optimizing quantum circuits, and a quantum information toolbox for creating advanced operators.
Qiskit provides two public APIs: a Python API and a C API. The Python API is the primary interface and prior to Qiskit 2.0 was the only public API available
for Qiskit. The C API is designed to provide direct access to Qiskit's internal data model (which is written in Rust). The C API can be consumed as either a
shared library (libqiskit.so) for standalone use, or from its embedding in the qiskit Python package for writing Python extension modules.
For more details on how to use Qiskit, refer to the documentation located here:
https://quantum.cloud.ibm.com/docs/
Installation
Python
For running Qiskit on Python we recommend installing Qiskit via pip:
pip install qiskit
Pip will handle all dependencies automatically and you will always install the latest (and well-tested) version.
To install from source, follow the instructions in the documentation.
Standalone C library
To install Qiskit as a standalone C library the only option is currently to build Qiskit from source. This requires having the Rust compiler installed. We recommend installing GNU Make to simplify the build process. With these requirements installed you can run:
make c
This will compile the C library and put the dist/c directory in the root of the repository which will contain the shared library and C headers for
the library.
You can refer to the documentation on installing the C API for more details and how to use the built library.
Create your first quantum program in Qiskit
Now that Qiskit is installed, it's time to begin working with Qiskit. We will use the Python interface to demonstrate creating a quantum program. The essential parts of a quantum program are:
- Define and build a quantum circuit that represents the quantum state
- Define the classical output by measurements or a set of observable operators
- Depending on the output, use the Sampler primitive to sample outcomes or the Estimator primitive to estimate expectation values.
Create an example quantum circuit using the QuantumCircuit class:
import numpy as np
from qiskit import QuantumCircuit
# 1. A quantum circuit for preparing the quantum state |000> + i |111> / √2
qc = QuantumCircuit(3)
qc.h(0) # generate superposition
qc.p(np.pi / 2, 0) # add quantum phase
qc.cx(0, 1) # 0th-qubit-Controlled-NOT gate on 1st qubit
qc.cx(0, 2) # 0th-qubit-Controlled-NOT gate on 2nd qubit
This simple example creates an entangled state known as a GHZ state $(|000\rangle + i|111\rangle)/\sqrt{2}$. It uses the standard quantum gates: Hadamard gate (h), Phase gate (p), and CNOT gate (cx).
Once you've made your first quantum circuit, choose which primitive you will use. Starting with the Sampler,
we use measure_all(inplace=False) to get a copy of the circuit in which all the qubits are measured:
# 2. Add the classical output in the form of measurement of all qubits
qc_measured = qc.measure_all(inplace=False)
# 3. Execute using the Sampler primitive
from qiskit.primitives import StatevectorSampler
sampler = StatevectorSampler()
job = sampler.run([qc_measured], shots=1000)
result = job.result()
print(f" > Counts: {result[0].data['meas'].get_counts()}")
Running this will give an outcome similar to {'000': 497, '111': 503} which is 000 50% of the time and 111 50% of the time up to statistical fluctuations.
To illustrate the power of the Estimator, we now use the quantum information toolbox to create the operator $XXY+XYX+YXX-YYY$ and pass it to the run() function, along with our quantum circuit. Note that the Estimator requires a circuit without measurements, so we use the qc circuit we created earlier.
# 2. Define the observable to be measured
from qiskit.quantum_info import SparsePauliOp
operator = SparsePauliOp.from_list([("XXY", 1), ("XYX", 1), ("YXX", 1), ("YYY", -1)])
# 3. Execute using the Estimator primitive
from qiskit.primitives import StatevectorEstimator
estimator = StatevectorEstimator()
job = estimator.run([(qc, operator)], precision=1e-3)
result = job.result()
print(f" > Expectation values: {result[0].data.evs}")
Running this will give the outcome 4. For fun, try to assign a value of +/- 1 to each single-qubit operator X and Y
and see if you can achieve this outcome. (Spoiler alert: this is not possible!)
Using the Qiskit-provided qiskit.primitives.StatevectorSampler and qiskit.primitives.StatevectorEstimator will not take you very far.
The power of quantum computing cannot be simulated on classical computers and you need to use real quantum hardware to scale to larger quantum circuits.
However, running a quantum circuit on hardware requires rewriting to the basis gates and connectivity of the quantum hardware.
The tool that does this is the transpiler, and Qiskit includes transpiler passes for synthesis, optimization, mapping, and scheduling.
However, it also includes a default compiler, which works very well in most examples.
The following code will map the example circuit to the basis_gates = ["cz", "sx", "rz"] and a
bidirectional linear chain of qubits $0 \leftrightarrow 1 \leftrightarrow 2$ with the
coupling_map = [[0, 1], [1, 0], [1, 2], [2, 1]].
from qiskit import transpile
from qiskit.transpiler import Target, CouplingMap
target = Target.from_configuration(
basis_gates=["cz", "sx", "rz"],
coupling_map=CouplingMap.from_line(3),
)
qc_transpiled = transpile(qc, target=target)
Executing your code on real quantum hardware
Qiskit provides an abstraction layer that lets users run quantum circuits on hardware from any vendor that provides a compatible interface.
The best way to use Qiskit is with a runtime environment that provides optimized implementations of Sampler and Estimator for a given hardware platform. This runtime may involve using pre- and post-processing, such as optimized transpiler passes with error suppression, error mitigation, and, eventually, error correction built in. A runtime implements the qiskit.primitives.BaseSamplerV2 and qiskit.primitives.BaseEstimatorV2 interfaces. For example,
some packages that provide implementations of a runtime primitive are:
Qiskit also provides a lower-level abstract interface for describing quantum backends. This interface, located in
qiskit.providers, defines an abstract BackendV2 class that providers can implement to represent their
hardware or simulators to Qiskit. The backend class includes a common interface for executing circuits on the backends; however, in this interface each provider may perform different types of pre- and post-processing and return outcomes that are vendor-defined. Some examples of published provider packages that interface with real hardware are:
- https://github.com/qiskit-community/qiskit-ionq
- https://github.com/qiskit-community/qiskit-aqt-provider
- https://github.com/qiskit-community/qiskit-braket-provider
- https://github.com/qiskit-community/qiskit-quantinuum-provider
- https://github.com/rigetti/qiskit-rigetti
You can refer to the documentation of these packages for further instructions on how to get access and use these systems.
Contribution Guidelines
If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. By participating, you are expected to uphold our code of conduct.
We use GitHub issues for tracking requests and bugs. Please
join the Qiskit Slack community for discussion, comments, and questions.
For questions related to running or using Qiskit, Stack Overflow has a qiskit.
For questions on quantum computing with Qiskit, use the qiskit tag in the Quantum Computing Stack Exchange (please, read first the guidelines on how to ask in that forum).
Authors and Citation
Qiskit 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.
Changelog and Release Notes
The changelog for a particular release is dynamically generated and gets
written to the release page on GitHub for each release. For example, you can
find the page for the 1.2.0 release here:
https://github.com/Qiskit/qiskit/releases/tag/1.2.0
The changelog for the current release can be found in the releases tab:
Additionally, as part of each release, detailed release notes are written to document in detail what has changed. This includes any documentation on potential breaking changes on upgrade, and new features. See all release notes here.
Acknowledgements
We acknowledge partial support for Qiskit development from the DOE Office of Science National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704.
License
Metadata
Release files for qiskit 2.6.0rc1
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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|---|---|---|---|
| qiskit-2.6.0rc1.tar.gz | 4.5 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| qiskit-2.6.0rc1-cp310-abi3-win_amd64.whl | CPython 3.10 | abi3 | Windows x86-64 | Details |
| qiskit-2.6.0rc1-cp310-abi3-manylinux_2_28_x86_64.whl | CPython 3.10 | abi3 | Linux glibc 2.28+ x86-64 | Details |
| qiskit-2.6.0rc1-cp310-abi3-manylinux_2_28_s390x.whl | CPython 3.10 | abi3 | Linux glibc 2.28+ IBM System/390x | Details |
| qiskit-2.6.0rc1-cp310-abi3-manylinux_2_28_ppc64le.whl | CPython 3.10 | abi3 | Linux glibc 2.28+ PowerPC 64-le | Details |
| qiskit-2.6.0rc1-cp310-abi3-manylinux_2_28_aarch64.whl | CPython 3.10 | abi3 | Linux glibc 2.28+ ARM64 | Details |
| qiskit-2.6.0rc1-cp310-abi3-macosx_11_0_arm64.whl | CPython 3.10 | abi3 | macOS 11.0+ ARM64 | Details |
| qiskit-2.6.0rc1-cp310-abi3-macosx_10_12_x86_64.whl | CPython 3.10 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 74.5 MB
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