%global _empty_manifest_terminate_build 0 Name: python-qiskit-ignis Version: 0.7.1 Release: 1 Summary: Qiskit tools for quantum information science License: Apache 2.0 URL: https://github.com/Qiskit/qiskit-ignis Source0: https://mirrors.nju.edu.cn/pypi/web/packages/93/d7/a077a5d828037667f4449623ae6c982cac498ebc47e5975ed74c17c15772/qiskit-ignis-0.7.1.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-qiskit-terra Requires: python3-retworkx Requires: python3-scipy Requires: python3-setuptools Requires: python3-cvxpy Requires: python3-scikit-learn Requires: python3-numba Requires: python3-matplotlib %description # Qiskit Ignis (_DEPRECATED_) [![License](https://img.shields.io/github/license/Qiskit/qiskit-ignis.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-ignis/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-ignis)[![](https://img.shields.io/github/release/Qiskit/qiskit-ignis.svg?style=popout-square)](https://github.com/Qiskit/qiskit-ignis/releases)[![](https://img.shields.io/pypi/dm/qiskit-ignis.svg?style=popout-square)](https://pypi.org/project/qiskit-ignis/) **_NOTE_** _As of the version 0.7.0 Qiskit Ignis is deprecated and has been superseded by the [Qiskit Experiments](https://github.com/Qiskit/qiskit-experiments) project. Active development on the project has stopped and only compatibility fixes and other critical bugfixes will be accepted until the project is officially retired and archived._ **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 **Ignis**, which provides tools for quantum hardware verification, noise characterization, and error correction. ## Migration Guide As of version 0.7.0, Qiskit Ignis has been deprecated and some of its functionality was migrated into the `qiskit-experiments` package and into `qiskit-terra`. * Ignis characterization module * This module was partly migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) and split into two different modules: `qiskit_experiments.library.calibration` `qiskit_experiments.library.characterization` * `AmpCal` is now replaced by `FineAmplitude`. * `ZZFitter` was not migrated yet. * Ignis discriminator module * This module is in the process of migration to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * Ignis mitigation module * The readout mitigator will be soon added to [`qiskit-terra`](https://github.com/Qiskit/qiskit-terra). * Experiments for generating the readout mitigators will be added to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * For use of mitigators with `qiskit.algorithms` and the [`QuantumInstance` class](https://qiskit.org/documentation/stubs/qiskit.utils.QuantumInstance.html?highlight=quantuminstance#qiskit.utils.QuantumInstance) this has been integrated into `qiskit-terra` directly with the `QuantumInstance`. * Ignis verification module * Randomized benchmarking, Quantum Volume and State and Process Tomography were migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments). * Migration of Gate-set tomography to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) is in progress. * `topological_codes` will continue development under [NCCR-SPIN](https://github.com/NCCR-SPIN/topological_codes/blob/master/README.md), while the functionality is reintegrated into Qiskit. Some additional functionality can also be found in the offshoot project [qtcodes](https://github.com/yaleqc/qtcodes). * Currently the Accredition and Entanglement modules have not been migrated. The following table gives a more detailed breakdown that relates the function, as it existed in Ignis, to where it now lives after this move. | Old | New | Library | | :---: | :---: | :---: | | qiskit.ignis.characterization.calibrations | qiskit_experiments.library.calibration | qiskit-experiments | | qiskit.ignis.characterization.coherence | qiskit_experiments.library.characterization | qiskit-experiments | | qiskit.ignis.mitigation | qiskit_terra.mitigation | qiskit-terra | | qiskit.ignis.verification.quantum_volume | qiskit_experiments.library.quantum_volume | qiskit-experiments | | qiskit.ignis.verification.randomized_benchmarking | qiskit_experiments.library.randomized_benchmarking | qiskit-experiments | | qiskit.ignis.verification.tomography | qiskit_experiments.library.tomography | qiskit-experiments | ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Ignis. ```bash pip install qiskit ``` 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). ### Extra Requirements Some functionality has extra optional requirements. If you're going to use any visualization functions for fitters you'll need to install matplotlib. You can do this with `pip install matplotlib` or when you install ignis with `pip install qiskit-ignis[visualization]`. If you're going to use a cvx fitter for running tomogography you'll need to install cvxpy. You can do this with `pip install cvxpy` or when you install ignis with `pip install qiskit-ignis[cvx]`. When performing expectation value measurement error mitigation using the CTMP method performance can be improved using just-in-time compiling if Numbda is installed. You can do this with `pip install numba` or when you install ignis with `pip install qiskit-ignis[jit]`. For using the discriminator classes in `qiskit.ignis.measurement` scikit-learn needs to be installed. You can do this with `pip install scikit-learn` or when you install ignis with `pip install qiskit-ignis[iq]`. If you want to install all extra requirements when you install ignis you can run `pip install qiskit-ignis[visualization,cvx,jit,iq]`. ## Creating your first quantum experiment with Qiskit Ignis Now that you have Qiskit Ignis installed, you can start creating experiments, to reveal information about the device quality. Here is a basic example: ``` $ python ``` ```python # Import Qiskit classes import qiskit from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister from qiskit.providers.aer import noise # import AER noise model # Measurement error mitigation functions from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter, MeasurementFilter) # Generate a noise model for the qubits noise_model = noise.NoiseModel() for qi in range(5): read_err = noise.errors.readout_error.ReadoutError([[0.75, 0.25],[0.1, 0.9]]) noise_model.add_readout_error(read_err, [qi]) # Generate the measurement calibration circuits # for running measurement error mitigation qr = QuantumRegister(5) meas_cals, state_labels = complete_meas_cal(qubit_list=[2,3,4], qr=qr) # Execute the calibration circuits backend = qiskit.Aer.get_backend('qasm_simulator') job = qiskit.execute(meas_cals, backend=backend, shots=1000, noise_model=noise_model) cal_results = job.result() # Make a calibration matrix meas_fitter = CompleteMeasFitter(cal_results, state_labels) # Make a 3Q GHZ state cr = ClassicalRegister(3) ghz = QuantumCircuit(qr, cr) ghz.h(qr[2]) ghz.cx(qr[2], qr[3]) ghz.cx(qr[3], qr[4]) ghz.measure(qr[2],cr[0]) ghz.measure(qr[3],cr[1]) ghz.measure(qr[4],cr[2]) # Execute the GHZ circuit (with the same noise model) job = qiskit.execute(ghz, backend=backend, shots=1000, noise_model=noise_model) results = job.result() # Results without mitigation raw_counts = results.get_counts() print("Results without mitigation:", raw_counts) # Create a measurement filter from the calibration matrix meas_filter = meas_fitter.filter # Apply the filter to the raw counts to mitigate # the measurement errors mitigated_counts = meas_filter.apply(raw_counts) print("Results with mitigation:", {l:int(mitigated_counts[l]) for l in mitigated_counts}) ``` ``` Results without mitigation: {'000': 181, '001': 83, '010': 59, '011': 65, '100': 101, '101': 48, '110': 72, '111': 391} Results with mitigation: {'000': 421, '001': 2, '011': 1, '100': 53, '110': 13, '111': 510} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit Ignis, 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-ignis/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://join.slack.com/t/qiskit/shared_invite/enQtNDc2NjUzMjE4Mzc0LTMwZmE0YTM4ZThiNGJmODkzN2Y2NTNlMDIwYWNjYzA2ZmM1YTRlZGQ3OGM0NjcwMjZkZGE0MTA4MGQ1ZTVmYzk). 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 Tutorials](https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/ignis) repository. ## Authors and Citation Qiskit Ignis is the work of [many people](https://github.com/Qiskit/qiskit-ignis/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-ignis Summary: Qiskit tools for quantum information science Provides: python-qiskit-ignis BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-qiskit-ignis # Qiskit Ignis (_DEPRECATED_) [![License](https://img.shields.io/github/license/Qiskit/qiskit-ignis.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-ignis/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-ignis)[![](https://img.shields.io/github/release/Qiskit/qiskit-ignis.svg?style=popout-square)](https://github.com/Qiskit/qiskit-ignis/releases)[![](https://img.shields.io/pypi/dm/qiskit-ignis.svg?style=popout-square)](https://pypi.org/project/qiskit-ignis/) **_NOTE_** _As of the version 0.7.0 Qiskit Ignis is deprecated and has been superseded by the [Qiskit Experiments](https://github.com/Qiskit/qiskit-experiments) project. Active development on the project has stopped and only compatibility fixes and other critical bugfixes will be accepted until the project is officially retired and archived._ **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 **Ignis**, which provides tools for quantum hardware verification, noise characterization, and error correction. ## Migration Guide As of version 0.7.0, Qiskit Ignis has been deprecated and some of its functionality was migrated into the `qiskit-experiments` package and into `qiskit-terra`. * Ignis characterization module * This module was partly migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) and split into two different modules: `qiskit_experiments.library.calibration` `qiskit_experiments.library.characterization` * `AmpCal` is now replaced by `FineAmplitude`. * `ZZFitter` was not migrated yet. * Ignis discriminator module * This module is in the process of migration to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * Ignis mitigation module * The readout mitigator will be soon added to [`qiskit-terra`](https://github.com/Qiskit/qiskit-terra). * Experiments for generating the readout mitigators will be added to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * For use of mitigators with `qiskit.algorithms` and the [`QuantumInstance` class](https://qiskit.org/documentation/stubs/qiskit.utils.QuantumInstance.html?highlight=quantuminstance#qiskit.utils.QuantumInstance) this has been integrated into `qiskit-terra` directly with the `QuantumInstance`. * Ignis verification module * Randomized benchmarking, Quantum Volume and State and Process Tomography were migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments). * Migration of Gate-set tomography to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) is in progress. * `topological_codes` will continue development under [NCCR-SPIN](https://github.com/NCCR-SPIN/topological_codes/blob/master/README.md), while the functionality is reintegrated into Qiskit. Some additional functionality can also be found in the offshoot project [qtcodes](https://github.com/yaleqc/qtcodes). * Currently the Accredition and Entanglement modules have not been migrated. The following table gives a more detailed breakdown that relates the function, as it existed in Ignis, to where it now lives after this move. | Old | New | Library | | :---: | :---: | :---: | | qiskit.ignis.characterization.calibrations | qiskit_experiments.library.calibration | qiskit-experiments | | qiskit.ignis.characterization.coherence | qiskit_experiments.library.characterization | qiskit-experiments | | qiskit.ignis.mitigation | qiskit_terra.mitigation | qiskit-terra | | qiskit.ignis.verification.quantum_volume | qiskit_experiments.library.quantum_volume | qiskit-experiments | | qiskit.ignis.verification.randomized_benchmarking | qiskit_experiments.library.randomized_benchmarking | qiskit-experiments | | qiskit.ignis.verification.tomography | qiskit_experiments.library.tomography | qiskit-experiments | ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Ignis. ```bash pip install qiskit ``` 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). ### Extra Requirements Some functionality has extra optional requirements. If you're going to use any visualization functions for fitters you'll need to install matplotlib. You can do this with `pip install matplotlib` or when you install ignis with `pip install qiskit-ignis[visualization]`. If you're going to use a cvx fitter for running tomogography you'll need to install cvxpy. You can do this with `pip install cvxpy` or when you install ignis with `pip install qiskit-ignis[cvx]`. When performing expectation value measurement error mitigation using the CTMP method performance can be improved using just-in-time compiling if Numbda is installed. You can do this with `pip install numba` or when you install ignis with `pip install qiskit-ignis[jit]`. For using the discriminator classes in `qiskit.ignis.measurement` scikit-learn needs to be installed. You can do this with `pip install scikit-learn` or when you install ignis with `pip install qiskit-ignis[iq]`. If you want to install all extra requirements when you install ignis you can run `pip install qiskit-ignis[visualization,cvx,jit,iq]`. ## Creating your first quantum experiment with Qiskit Ignis Now that you have Qiskit Ignis installed, you can start creating experiments, to reveal information about the device quality. Here is a basic example: ``` $ python ``` ```python # Import Qiskit classes import qiskit from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister from qiskit.providers.aer import noise # import AER noise model # Measurement error mitigation functions from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter, MeasurementFilter) # Generate a noise model for the qubits noise_model = noise.NoiseModel() for qi in range(5): read_err = noise.errors.readout_error.ReadoutError([[0.75, 0.25],[0.1, 0.9]]) noise_model.add_readout_error(read_err, [qi]) # Generate the measurement calibration circuits # for running measurement error mitigation qr = QuantumRegister(5) meas_cals, state_labels = complete_meas_cal(qubit_list=[2,3,4], qr=qr) # Execute the calibration circuits backend = qiskit.Aer.get_backend('qasm_simulator') job = qiskit.execute(meas_cals, backend=backend, shots=1000, noise_model=noise_model) cal_results = job.result() # Make a calibration matrix meas_fitter = CompleteMeasFitter(cal_results, state_labels) # Make a 3Q GHZ state cr = ClassicalRegister(3) ghz = QuantumCircuit(qr, cr) ghz.h(qr[2]) ghz.cx(qr[2], qr[3]) ghz.cx(qr[3], qr[4]) ghz.measure(qr[2],cr[0]) ghz.measure(qr[3],cr[1]) ghz.measure(qr[4],cr[2]) # Execute the GHZ circuit (with the same noise model) job = qiskit.execute(ghz, backend=backend, shots=1000, noise_model=noise_model) results = job.result() # Results without mitigation raw_counts = results.get_counts() print("Results without mitigation:", raw_counts) # Create a measurement filter from the calibration matrix meas_filter = meas_fitter.filter # Apply the filter to the raw counts to mitigate # the measurement errors mitigated_counts = meas_filter.apply(raw_counts) print("Results with mitigation:", {l:int(mitigated_counts[l]) for l in mitigated_counts}) ``` ``` Results without mitigation: {'000': 181, '001': 83, '010': 59, '011': 65, '100': 101, '101': 48, '110': 72, '111': 391} Results with mitigation: {'000': 421, '001': 2, '011': 1, '100': 53, '110': 13, '111': 510} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit Ignis, 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-ignis/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://join.slack.com/t/qiskit/shared_invite/enQtNDc2NjUzMjE4Mzc0LTMwZmE0YTM4ZThiNGJmODkzN2Y2NTNlMDIwYWNjYzA2ZmM1YTRlZGQ3OGM0NjcwMjZkZGE0MTA4MGQ1ZTVmYzk). 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 Tutorials](https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/ignis) repository. ## Authors and Citation Qiskit Ignis is the work of [many people](https://github.com/Qiskit/qiskit-ignis/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-ignis Provides: python3-qiskit-ignis-doc %description help # Qiskit Ignis (_DEPRECATED_) [![License](https://img.shields.io/github/license/Qiskit/qiskit-ignis.svg?style=popout-square)](https://opensource.org/licenses/Apache-2.0)[![Build Status](https://img.shields.io/travis/com/Qiskit/qiskit-ignis/master.svg?style=popout-square)](https://travis-ci.com/Qiskit/qiskit-ignis)[![](https://img.shields.io/github/release/Qiskit/qiskit-ignis.svg?style=popout-square)](https://github.com/Qiskit/qiskit-ignis/releases)[![](https://img.shields.io/pypi/dm/qiskit-ignis.svg?style=popout-square)](https://pypi.org/project/qiskit-ignis/) **_NOTE_** _As of the version 0.7.0 Qiskit Ignis is deprecated and has been superseded by the [Qiskit Experiments](https://github.com/Qiskit/qiskit-experiments) project. Active development on the project has stopped and only compatibility fixes and other critical bugfixes will be accepted until the project is officially retired and archived._ **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 **Ignis**, which provides tools for quantum hardware verification, noise characterization, and error correction. ## Migration Guide As of version 0.7.0, Qiskit Ignis has been deprecated and some of its functionality was migrated into the `qiskit-experiments` package and into `qiskit-terra`. * Ignis characterization module * This module was partly migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) and split into two different modules: `qiskit_experiments.library.calibration` `qiskit_experiments.library.characterization` * `AmpCal` is now replaced by `FineAmplitude`. * `ZZFitter` was not migrated yet. * Ignis discriminator module * This module is in the process of migration to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * Ignis mitigation module * The readout mitigator will be soon added to [`qiskit-terra`](https://github.com/Qiskit/qiskit-terra). * Experiments for generating the readout mitigators will be added to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) * For use of mitigators with `qiskit.algorithms` and the [`QuantumInstance` class](https://qiskit.org/documentation/stubs/qiskit.utils.QuantumInstance.html?highlight=quantuminstance#qiskit.utils.QuantumInstance) this has been integrated into `qiskit-terra` directly with the `QuantumInstance`. * Ignis verification module * Randomized benchmarking, Quantum Volume and State and Process Tomography were migrated to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments). * Migration of Gate-set tomography to [`qiskit-experiments`](https://github.com/Qiskit/qiskit-experiments) is in progress. * `topological_codes` will continue development under [NCCR-SPIN](https://github.com/NCCR-SPIN/topological_codes/blob/master/README.md), while the functionality is reintegrated into Qiskit. Some additional functionality can also be found in the offshoot project [qtcodes](https://github.com/yaleqc/qtcodes). * Currently the Accredition and Entanglement modules have not been migrated. The following table gives a more detailed breakdown that relates the function, as it existed in Ignis, to where it now lives after this move. | Old | New | Library | | :---: | :---: | :---: | | qiskit.ignis.characterization.calibrations | qiskit_experiments.library.calibration | qiskit-experiments | | qiskit.ignis.characterization.coherence | qiskit_experiments.library.characterization | qiskit-experiments | | qiskit.ignis.mitigation | qiskit_terra.mitigation | qiskit-terra | | qiskit.ignis.verification.quantum_volume | qiskit_experiments.library.quantum_volume | qiskit-experiments | | qiskit.ignis.verification.randomized_benchmarking | qiskit_experiments.library.randomized_benchmarking | qiskit-experiments | | qiskit.ignis.verification.tomography | qiskit_experiments.library.tomography | qiskit-experiments | ## Installation We encourage installing Qiskit via the pip tool (a python package manager). The following command installs the core Qiskit components, including Ignis. ```bash pip install qiskit ``` 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). ### Extra Requirements Some functionality has extra optional requirements. If you're going to use any visualization functions for fitters you'll need to install matplotlib. You can do this with `pip install matplotlib` or when you install ignis with `pip install qiskit-ignis[visualization]`. If you're going to use a cvx fitter for running tomogography you'll need to install cvxpy. You can do this with `pip install cvxpy` or when you install ignis with `pip install qiskit-ignis[cvx]`. When performing expectation value measurement error mitigation using the CTMP method performance can be improved using just-in-time compiling if Numbda is installed. You can do this with `pip install numba` or when you install ignis with `pip install qiskit-ignis[jit]`. For using the discriminator classes in `qiskit.ignis.measurement` scikit-learn needs to be installed. You can do this with `pip install scikit-learn` or when you install ignis with `pip install qiskit-ignis[iq]`. If you want to install all extra requirements when you install ignis you can run `pip install qiskit-ignis[visualization,cvx,jit,iq]`. ## Creating your first quantum experiment with Qiskit Ignis Now that you have Qiskit Ignis installed, you can start creating experiments, to reveal information about the device quality. Here is a basic example: ``` $ python ``` ```python # Import Qiskit classes import qiskit from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister from qiskit.providers.aer import noise # import AER noise model # Measurement error mitigation functions from qiskit.ignis.mitigation.measurement import (complete_meas_cal, CompleteMeasFitter, MeasurementFilter) # Generate a noise model for the qubits noise_model = noise.NoiseModel() for qi in range(5): read_err = noise.errors.readout_error.ReadoutError([[0.75, 0.25],[0.1, 0.9]]) noise_model.add_readout_error(read_err, [qi]) # Generate the measurement calibration circuits # for running measurement error mitigation qr = QuantumRegister(5) meas_cals, state_labels = complete_meas_cal(qubit_list=[2,3,4], qr=qr) # Execute the calibration circuits backend = qiskit.Aer.get_backend('qasm_simulator') job = qiskit.execute(meas_cals, backend=backend, shots=1000, noise_model=noise_model) cal_results = job.result() # Make a calibration matrix meas_fitter = CompleteMeasFitter(cal_results, state_labels) # Make a 3Q GHZ state cr = ClassicalRegister(3) ghz = QuantumCircuit(qr, cr) ghz.h(qr[2]) ghz.cx(qr[2], qr[3]) ghz.cx(qr[3], qr[4]) ghz.measure(qr[2],cr[0]) ghz.measure(qr[3],cr[1]) ghz.measure(qr[4],cr[2]) # Execute the GHZ circuit (with the same noise model) job = qiskit.execute(ghz, backend=backend, shots=1000, noise_model=noise_model) results = job.result() # Results without mitigation raw_counts = results.get_counts() print("Results without mitigation:", raw_counts) # Create a measurement filter from the calibration matrix meas_filter = meas_fitter.filter # Apply the filter to the raw counts to mitigate # the measurement errors mitigated_counts = meas_filter.apply(raw_counts) print("Results with mitigation:", {l:int(mitigated_counts[l]) for l in mitigated_counts}) ``` ``` Results without mitigation: {'000': 181, '001': 83, '010': 59, '011': 65, '100': 101, '101': 48, '110': 72, '111': 391} Results with mitigation: {'000': 421, '001': 2, '011': 1, '100': 53, '110': 13, '111': 510} ``` ## Contribution Guidelines If you'd like to contribute to Qiskit Ignis, 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-ignis/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://join.slack.com/t/qiskit/shared_invite/enQtNDc2NjUzMjE4Mzc0LTMwZmE0YTM4ZThiNGJmODkzN2Y2NTNlMDIwYWNjYzA2ZmM1YTRlZGQ3OGM0NjcwMjZkZGE0MTA4MGQ1ZTVmYzk). 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 Tutorials](https://github.com/Qiskit/qiskit-iqx-tutorials/tree/master/qiskit/advanced/ignis) repository. ## Authors and Citation Qiskit Ignis is the work of [many people](https://github.com/Qiskit/qiskit-ignis/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-ignis-0.7.1 %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-ignis -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Apr 11 2023 Python_Bot - 0.7.1-1 - Package Spec generated