%global _empty_manifest_terminate_build 0 Name: python-qiskit-aer Version: 0.12.0 Release: 1 Summary: Qiskit Aer - High performance simulators for Qiskit License: Apache 2.0 URL: https://github.com/Qiskit/qiskit-aer Source0: https://mirrors.nju.edu.cn/pypi/web/packages/03/96/ea1988dac83a1cd5a28576a79fdc5f12fec2025b4a5a8ad500592f142dde/qiskit-aer-0.12.0.tar.gz Requires: python3-qiskit-terra Requires: python3-numpy Requires: python3-scipy Requires: python3-dask Requires: python3-distributed %description # Qiskit Aer [![License](https://img.shields.io/github/license/Qiskit/qiskit-aer.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-aer/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-aer)[![](https://img.shields.io/github/release/Qiskit/qiskit-aer.svg?style=popout-square)](https://github.com/Qiskit/qiskit-aer/releases)[![](https://img.shields.io/pypi/dm/qiskit-aer.svg?style=popout-square)](https://pypi.org/project/qiskit-aer/) **Qiskit** is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms. Qiskit is made up of elements that each work together to enable quantum computing. This element is **Aer**, which provides high-performance quantum computing simulators with realistic noise models. ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Aer. ```bash pip install qiskit 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® 10.1 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 ``` 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 quantum program with Qiskit Aer Now that you have Qiskit Aer installed, you can start simulating quantum circuits with noise. Here is a basic example: ``` $ python ``` ```python import qiskit from qiskit import IBMQ from qiskit_aer import AerSimulator # Generate 3-qubit GHZ state circ = qiskit.QuantumCircuit(3) circ.h(0) circ.cx(0, 1) circ.cx(1, 2) circ.measure_all() # Construct an ideal simulator aersim = AerSimulator() # Perform an ideal simulation result_ideal = qiskit.execute(circ, aersim).result() counts_ideal = result_ideal.get_counts(0) print('Counts(ideal):', counts_ideal) # Counts(ideal): {'000': 493, '111': 531} # Construct a noisy simulator backend from an IBMQ backend # This simulator backend will be automatically configured # using the device configuration and noise model provider = IBMQ.load_account() backend = provider.get_backend('ibmq_athens') aersim_backend = AerSimulator.from_backend(backend) # Perform noisy simulation result_noise = qiskit.execute(circ, aersim_backend).result() counts_noise = result_noise.get_counts(0) print('Counts(noise):', counts_noise) # Counts(noise): {'000': 492, '001': 6, '010': 8, '011': 14, '100': 3, '101': 14, '110': 18, '111': 469} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit, 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 expect to uphold to 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 our [Qiskit IQX Tutorials](https://github.com/Qiskit/qiskit-tutorials/tree/master/tutorials/simulators) or [Qiskit Community Tutorials](https://github.com/Qiskit/qiskit-community-tutorials/tree/master/aer) repositories. ## Authors and Citation Qiskit 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/master/Qiskit.bib). ## License [Apache License 2.0](LICENSE.txt) %package -n python3-qiskit-aer Summary: Qiskit Aer - High performance simulators for Qiskit Provides: python-qiskit-aer BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-qiskit-aer # Qiskit Aer [![License](https://img.shields.io/github/license/Qiskit/qiskit-aer.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-aer/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-aer)[![](https://img.shields.io/github/release/Qiskit/qiskit-aer.svg?style=popout-square)](https://github.com/Qiskit/qiskit-aer/releases)[![](https://img.shields.io/pypi/dm/qiskit-aer.svg?style=popout-square)](https://pypi.org/project/qiskit-aer/) **Qiskit** is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms. Qiskit is made up of elements that each work together to enable quantum computing. This element is **Aer**, which provides high-performance quantum computing simulators with realistic noise models. ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Aer. ```bash pip install qiskit 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® 10.1 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 ``` 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 quantum program with Qiskit Aer Now that you have Qiskit Aer installed, you can start simulating quantum circuits with noise. Here is a basic example: ``` $ python ``` ```python import qiskit from qiskit import IBMQ from qiskit_aer import AerSimulator # Generate 3-qubit GHZ state circ = qiskit.QuantumCircuit(3) circ.h(0) circ.cx(0, 1) circ.cx(1, 2) circ.measure_all() # Construct an ideal simulator aersim = AerSimulator() # Perform an ideal simulation result_ideal = qiskit.execute(circ, aersim).result() counts_ideal = result_ideal.get_counts(0) print('Counts(ideal):', counts_ideal) # Counts(ideal): {'000': 493, '111': 531} # Construct a noisy simulator backend from an IBMQ backend # This simulator backend will be automatically configured # using the device configuration and noise model provider = IBMQ.load_account() backend = provider.get_backend('ibmq_athens') aersim_backend = AerSimulator.from_backend(backend) # Perform noisy simulation result_noise = qiskit.execute(circ, aersim_backend).result() counts_noise = result_noise.get_counts(0) print('Counts(noise):', counts_noise) # Counts(noise): {'000': 492, '001': 6, '010': 8, '011': 14, '100': 3, '101': 14, '110': 18, '111': 469} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit, 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 expect to uphold to 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 our [Qiskit IQX Tutorials](https://github.com/Qiskit/qiskit-tutorials/tree/master/tutorials/simulators) or [Qiskit Community Tutorials](https://github.com/Qiskit/qiskit-community-tutorials/tree/master/aer) repositories. ## Authors and Citation Qiskit 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/master/Qiskit.bib). ## License [Apache License 2.0](LICENSE.txt) %package help Summary: Development documents and examples for qiskit-aer Provides: python3-qiskit-aer-doc %description help # Qiskit Aer [![License](https://img.shields.io/github/license/Qiskit/qiskit-aer.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-aer/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-aer)[![](https://img.shields.io/github/release/Qiskit/qiskit-aer.svg?style=popout-square)](https://github.com/Qiskit/qiskit-aer/releases)[![](https://img.shields.io/pypi/dm/qiskit-aer.svg?style=popout-square)](https://pypi.org/project/qiskit-aer/) **Qiskit** is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms. Qiskit is made up of elements that each work together to enable quantum computing. This element is **Aer**, which provides high-performance quantum computing simulators with realistic noise models. ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Aer. ```bash pip install qiskit 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® 10.1 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 ``` 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 quantum program with Qiskit Aer Now that you have Qiskit Aer installed, you can start simulating quantum circuits with noise. Here is a basic example: ``` $ python ``` ```python import qiskit from qiskit import IBMQ from qiskit_aer import AerSimulator # Generate 3-qubit GHZ state circ = qiskit.QuantumCircuit(3) circ.h(0) circ.cx(0, 1) circ.cx(1, 2) circ.measure_all() # Construct an ideal simulator aersim = AerSimulator() # Perform an ideal simulation result_ideal = qiskit.execute(circ, aersim).result() counts_ideal = result_ideal.get_counts(0) print('Counts(ideal):', counts_ideal) # Counts(ideal): {'000': 493, '111': 531} # Construct a noisy simulator backend from an IBMQ backend # This simulator backend will be automatically configured # using the device configuration and noise model provider = IBMQ.load_account() backend = provider.get_backend('ibmq_athens') aersim_backend = AerSimulator.from_backend(backend) # Perform noisy simulation result_noise = qiskit.execute(circ, aersim_backend).result() counts_noise = result_noise.get_counts(0) print('Counts(noise):', counts_noise) # Counts(noise): {'000': 492, '001': 6, '010': 8, '011': 14, '100': 3, '101': 14, '110': 18, '111': 469} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit, 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 expect to uphold to 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 our [Qiskit IQX Tutorials](https://github.com/Qiskit/qiskit-tutorials/tree/master/tutorials/simulators) or [Qiskit Community Tutorials](https://github.com/Qiskit/qiskit-community-tutorials/tree/master/aer) repositories. ## Authors and Citation Qiskit 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/master/Qiskit.bib). ## License [Apache License 2.0](LICENSE.txt) %prep %autosetup -n qiskit-aer-0.12.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-qiskit-aer -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.12.0-1 - Package Spec generated