Saravanan666/pypi-org huggingface dataset:
Package: qip-installer
Qip is a Quarantine Installer for Python built overPipandWiz.It usesPipcommands to query and install Python
packages with its dependencies to specific locations in an isolated manner.>>>qipinstallscipyinfo:Requested'scipy'info:Installed'scipy-1.5.2'.info:Wizdefinitioncreatedfor'scipy-1.5.2'.info:Requested'numpy>=1.14.5'[from'scipy-1.5.2'].info:Installed'numpy-1.19.2'.info:Wizdefinitioncreatedfor'numpy-1.19.2'.info:Packagesinstalled:numpy-1.19.2,scipy-1.5.2info:Packageoutputdirectory:'/tmp/qip/packages'info:Definitionoutputdirectory:'/tmp/qip/definitions'AWizdefinition is created
for each package installed in order to safely use it within a protected
environment.>>>wiz-add/tmp/qip/definitionsusescipy--pythonDocumentationFull documentation, including installation and setup guides, can be found athttps://qip.readthedocs.io/en/stable/CopyrightCopyright (C) 2018, The MillThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.
Package: qi.portlet.TagClouds
Introductionqi.portlet.TagClouds is a plone product that adds tag cloud portlet support. The following parameters of the portlet are configurable through the web:portlet titlenumber of different tag sizesmaximum tags to showcontent types searched (optionally)tags (subjects) searched (optionally)section of the site to be searchedworkflow states searchedfiltering by keywords so that a tag cloud of all keywords that are combined
with the filter keywords is shownqi.portlet.TagClouds also comes with a simple caching mechanism. Cache remains
valid for a time interval that can be set in the portlet settings.Changelog1.35Update buildout.cfg to Plone 4.2, gitignore build products [gyst]Change import to work with Plone 4.1.x and Plone 4.2.x [lccruz]1.34Moved collective.testcaselayer from the install_requires to a ‘test’
extras_require. [maurits]Do not require cmf.ManagePortal to add or edit the portlet [erral]1.33Enable (language-independent) content across multiple INavigationRoot
Folders to be searched for tags [gyst]Change import of IVocabularyFactory in order to work with Plone 4.0.x and
Plone 4.1.x [erico_andrei]Added Brazilian Portuguese translation [erico_andrei]1.32Removed the member id from the cache key in portlet. Credits to dimo for
reporting. [ggozad]Switched to using native plone vocabulary for workflow states on portlet
edit form. [piv]Remove old file structure now that everything moved to src/ subfolder.
[kdeldycke]Added german translation [kiwisauce]1.31Fixed a case where the edit form is invoked and a previously selected
keyword does not exist anymore. [ggozad]Fixed cache key for multiple sites. Thanks to Guido Stevens [ggozad]Plone 4 compatibility. [ggozad]Moved to using collective.testcaselayer for testing. [ggozad]1.30The product is now accompanied with proper tests. [ggozad]Added filtering by keyword. Thanks to lzdych for the idea and
discussions. [ggozad]All important parameters (workflow states, portal types and search path)
are now present in the search links. [ggozad]Moved vocabularies. [ggozad]Removed the settings shouldRestrictBySubject and shouldRestrictByTypes.
Now just using sane defaults. [ggozad]Modified the caching mechanism, so that it takes into account the portlet
settings. This ensures separate caching for separate portlets. [ggozad]Added the member in the portlet cache key. This will increase the
calculations necessary, but is important as it was possible to cache private
objects as well. [ggozad]Added the number of items found under the tag in thetitleof the links.
[lzdych]Up french translation with new msgids. [toutpt]Add translation of workflow states vocabulary. [toutpt]1.21Fixed caching policy for multilingual sites, resolves: #2 [lzdych]Added quoting of tag links: fixes not working search by tags with special
chars. [lzdych]Added czech translation. [lzdych]extended html markup to support rounded corners, resolves: #1. [lzdych]1.20Added french translation. [toutpt]1.11Added workflow states to the configuration options. [ggozad]Added maximum tags to display to the configuration options. [ggozad]1.1Added root folder to search under. [ggozad]1.0Initial release [ggozad]
Package: qiq
qiq is the thin layer over pip with some principles.requirements.txt becomes qiq.txtvirtual environment created in project invenvevery package inqiq.txthas exact version or links to local directorycan make symlinks for local developmentNo requirements exceptPython 3.UsageFor exampleMy-Company-Coreis the Company's core library used almost in all projects. It's development files located relative to current project is../my_company_core.
Ourqiq.txtwill be:+dev:../my_company_core
My-Company-Core==1.2.7
Flask==1.0.2
flask-cors==3.0.7+devis the flag, which can be given inqiqcommand.
This will symlinkmy_company_coreto../my_company_core:qiq +devAnd this will install production use code to venv:qiqBy default used library name is last directory in path. It can be overriden with name and==:+dev:Company-Core==../my_company_coreVCS also supported:+dev:git+ssh://gitlab.com/myteam/mycompanycore.git@2313ceee
Package: qiscus-sdk
Qiscus SDK WrapperPython version of Qiscus SDK wrapperFree software: MIT licenseDocumentation:https://qiscus-sdk.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2017-06-07)First release on PyPI.
Package: qiskit2quafu
IntroductionThis package converts the circuit of qiskit into a circuit supported by quafu.
The converted circuit can be input to the cloud platform for processing.
To upload the task, refer tohttps://scq-cloud.github.io/In addition, it also supports Hamilton measurement and Variational optimizationTurtorialThe package provides different examples that cover various functions within the package
The example is located in the tutorial folder:FileFunctionqiskit2quafu.ipynbQuafu circuit conversion functionhamiltonTest.ipynbHamilton measurementhamiltonOptimization.ipynbHamilton variational optimization
Package: qiskit-algorithms
Qiskit AlgorithmsInstallationWe encourage installing Qiskit Algorithms via the pip tool (a python package manager).pipinstallqiskit-algorithmspipwill handle all dependencies automatically and you will always install the latest
(and well-tested) version.If you want to work on the very latest work-in-progress versions, either to try features ahead of
their official release or if you want to contribute to Algorithms, then you can install from source.
To do this follow the instructions in thedocumentation.Optional InstallsSome optimization algorithms require specific libraries to be run:Scikit-quant, may be installed using the commandpip install scikit-quant.SnobFit, may be installed using the commandpip install SQSnobFit.NLOpt, may be installed using the commandpip install nlopt.Contribution GuidelinesIf you'd like to contribute to Qiskit Algorithms, please take a look at ourcontribution guidelines.
This project adheres to Qiskit'scode of conduct.
By participating, you are expected to uphold this code.We useGitHub issuesfor tracking requests and bugs. Pleasejoin the Qiskit Slack communityand for discussion and simple questions.
For questions that are more suited for a forum, we use theQiskittag inStack Overflow.Authors and CitationQiskit Algorithms was inspired, authored and brought about by the collective work of a team of researchers.
Algorithms continues to grow with the help and work ofmany people, who contribute
to the project at different levels.
If you use Qiskit, please cite as per the providedBibTeX file.LicenseThis project uses theApache License 2.0.
Package: qiskit-alice-bob-provider
Alice & Bob Qiskit providerThis project contains a provider that allows access toAlice & BobQPUs and emulators using
the Qiskit framework.Full documentationis available hereand sample notebooks using the providerare available here.InstallationYou can install the provider usingpip:pipinstallqiskit-alice-bob-providerpipwill handle installing all the python dependencies automatically and you
will always install the  latest (and well-tested) version.Remote execution on Alice & Bob QPUs: use your API keyTo obtain an API key, pleasecontact Alice & Bob.You can initialize the Alice & Bob remote provider using your API key
locally with:fromqiskit_alice_bob_providerimportAliceBobRemoteProviderab=AliceBobRemoteProvider('MY_API_KEY')WhereMY_API_KEYis your API key to the Alice & Bob API.print(ab.backends())backend=ab.get_backend('EMU:1Q:LESCANNE_2020')The backend can then be used like a regular Qiskit backend:fromqiskitimportQuantumCircuit,executec=QuantumCircuit(1,2)c.initialize('+',0)c.measure_x(0,0)c.measure(0,1)job=execute(c,backend)res=job.result()print(res.get_counts())Local emulation of cat quit processorsThis project contains multiple emulators of multi cat qubit processors.fromqiskit_alice_bob_providerimportAliceBobLocalProviderfromqiskitimportQuantumCircuit,execute,transpileprovider=AliceBobLocalProvider()print(provider.backends())# EMU:6Q:PHYSICAL_CATS, EMU:40Q:PHYSICAL_CATS, EMU:1Q:LESCANNE_2020TheEMU:nQ:PHYSICAL_CATSbackends are theoretical models of quantum processors made
up of physical cat qubits.
They can be used to study the properties of error correction codes implemented
with physical cat qubits, for different hardware performance levels
(see the parameters of classPhysicalCatProcessor).TheEMU:1Q:LESCANNE_2020backend is an interpolated model simulating the processor
used in theseminal paperby Raphaël
Lescanne in 2020.
This interpolated model is configured to act as a digital twin of the cat qubit
used in this paper.
It does not represent the current performance of Alice & Bob's cat qubits.The example below schedules and simulates a Bell state preparation circuit on
aEMU:6Q:PHYSICAL_CATSprocessor, for different values of parametersaverage_nb_photonsandkappa_2.fromqiskit_alice_bob_providerimportAliceBobLocalProviderfromqiskitimportQuantumCircuit,execute,transpileprovider=AliceBobLocalProvider()circ=QuantumCircuit(2,2)circ.initialize('0+')circ.cx(0,1)circ.measure(0,0)circ.measure(1,1)# Default 6-qubit QPU with the ratio of memory dissipation rates set to# k1/k2=1e-5 and cat size, average_nb_photons, set to 16.backend=provider.get_backend('EMU:6Q:PHYSICAL_CATS')print(transpile(circ,backend).draw())# *Displays a timed and scheduled circuit*print(execute(circ,backend,shots=100000).result().get_counts())# {'11': 49823, '00': 50177}# Changing the cat size from 16 (default) to 4 and k1/k2 to 1e-2.backend=provider.get_backend('EMU:6Q:PHYSICAL_CATS',average_nb_photons=4,kappa_2=1e4)print(execute(circ,backend,shots=100000).result().get_counts())# {'01': 557, '11': 49422, '10': 596, '00': 49425}
Package: qiskit-aqua
Qiskit Aquais an extensible,
modular, open-source library of quantum computing algorithms.
Researchers can experiment with Aqua algorithms, on near-term quantum devices and simulators,
and can also get involved by contributing new algorithms and algorithm-supporting objects,
such as optimizers and variational forms.
Qiskit Aqua also contains application domain support in the form of the chemistry, finance,
machine learning and optimization modules to experiment with real-world applications to quantum computing.
Package: qiskit-aws-braket-provider
IBM's qiskit provider for AWS Braket (qiskit-aws-braket-provider)Attention: this is a work-in-progess implementation.What this means: it basically works. But:edge cases are not coveredeven broad cases aren't entirely covered!!in code documentation is not threno usage or API documentation existsCI/CD is not setupSo: use at your own risk.ContributingWe welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributers to this plugin will be listed as authors on the releases.We also encourage bug reports, suggestions for new features and enhancements!AuthorsCarsten BlankSupportSource Code:https://github.com/carstenblank/qiskit-aws-braket-providerIssue Tracker:https://github.com/carstenblank/qiskit-aws-braket-provider/issuesIf you are having issues, please let us know by posting the issue on our Github issue tracker.LicenseThe qiskit-aws-braket-provider is free and open source, released under the Apache License, Version 2.0.
Package: qiskit-bip-mapper
Qiskit BIPMapping PluginThis repository contains a standalone routing stage to use theBIPMappingrouting pass. The BIP mapping pass solves theroutingandlayoutproblems as a binary integer programming (BIP) problem. The algorithm used
in this pass is described in:G. Nannicini et al. "Optimal qubit assignment and routing via integer programming."arXiv:2106.06446This plugin depends onCPLEXto solve the BIP problem. While a no-cost version of CPLEX is available (and published onPyPI) this has limits set on the size of the problems
it can solve which prevents it from being used except for very small quantum circuits. If
you would like to use this transpiler pass for larger circuits a CPLEX license will be
required.Install and Use pluginTo use the unitary synthesis plugin first install qiskit terra with the pull
request:pipinstallqiskit-bip-mapperTo install the plugin package. As part of the install processpipwill install
the no-cost version of CPLEX from PyPI automatically. However, if you're going to
use the qiskit-bip-mapper plugin for runningtranspile()on circuits more than
a couple qubits or with more than handful of 2 qubit gates you will likely need
to install the commercial version of CPLEX.Using BIPMapping passOnce you have the plugin package installed you can use the plugin via therouting_method="bip"argument on Qiskit'stranspile()function. For example,
if you wanted to use theBIPMappingmethod to compile a 15 qubit quantum
volume circuit for a backend you would do something like:fromqiskitimporttranspilefromqiskit.circuit.libraryimportQuantumVolumefromqiskit.providers.fake_providerimportFakePragueqc=QuantumVolume(15)qc.measure_all()backend=FakePrague()transpile(qc,backend,routing_method="bip")Authors and CitationThe qiskit-bip-mapper is the work ofmany peoplewho contribute to the project at different levels. Additionally, the plugin was
originally developed as part of the Qiskit project itself and you can see the
development history for it here:https://github.com/Qiskit/qiskit-terra/commits/0.23.3/qiskit/transpiler/passes/routing/bip_mapping.pyhttps://github.com/Qiskit/qiskit-terra/commits/0.23.3/qiskit/transpiler/passes/routing/algorithms/bip_model.pyIf you useqiskit-bip-mapperin your research, please cite our paper as per the includedBibTeX filefile.
Package: qiskit-braket-provider
Qiskit-Braket providerQiskit-Braket provider to execute Qiskit programs on AWS quantum computing hardware devices through Amazon Braket.Table of ContentsFor UsersInstallationQuickstart GuideTutorialsHow-TosHow to Give FeedbackContribution GuidelinesReferences and AcknowledgementsLicenseFor Developers/ContributorsContribution GuideHow to Give FeedbackWe encourage your feedback! You can share your thoughts with us by:Opening an issuein the repositoryContribution GuidelinesFor information on how to contribute to this project, please take a look at ourcontribution guidelines.References and Acknowledgements[1] Qiskithttps://www.ibm.com/quantum/qiskit[2] Amazon Braket Python SDKhttps://github.com/aws/amazon-braket-sdk-pythonLicenseApache License 2.0
Package: qiskit-classroom
Qiskit-ClassroomQiskit-classroom is a toolkit that helps implement quantum algorithms by converting and visualizing different expressions used in the Qiskit ecosystem using Qiskit-classroom-converter. The following three transformations are supportedQuantum Circuit to Dirac notationQuantum Circuit to MatrixMatrix to Quantum CircuitGetting StartedPrerequisitesLaTeX's distribution(or program) must be installedOn GNU/Linux recommend TeX LiveOn Windows recommend MiKTeXgit should be installedpython must be installed (3.9 <= X <= 3.11)Qt6(>= 6.0.x) must be installedInstall with Flatpak (GNU/Linux)We're currently packaging flatpak package. please wait for a couple of daysInstall with PyPi (Windows, macOS)pip install qiskit-classroomwarningApple Silicon Processor not supported read this articleyou must install latex distribution(program).How to debug# download packagegithttps://github.com/KMU-quantum-classrooom/qiksit-classroom.git# install python packagescdqiskit-classroom
pipinstall-rrequirements.txt# run scriptspython-mmain.pyScreenShotsmain windowQuantum CircuitIt takes in the Python code and the names of the variables in the circuit you want to convert.
The Python code can be imported by dragging and dropping or by importing a file.MatrixTakes in a matrix written in Python syntax, the number of qubits, and whether the circuit is observed or not.Acknowledgement국문 : "본 연구는 2022년 과학기술정보통신부 및 정보통신기획평가원의 SW중심대학사업의 연구결과로 수행되었음"(2022-0-00964)English : "This research was supported by the MIST(Ministry of Science, ICT), Korea, under the National Program for Excellence in SW), supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation) in 2022"(2022-0-00964)LicenseQiskit-Classroom is licensed under the Apache License, Version 2.0
Package: qiskit-classroom-converter
qiskit-classroom-converterQiskit classroom ConverterDocumentshttps://kmu-quantum-classroom.github.io/qiskit-classroom-converter/qiskit_class_converter.htmlOfficial USER Guidehttps://kmu-quantum-classroom.github.io/documents/overview.htmlSupport convert methodquantum circuit to bra-ket notationquantum circuit to matrixmatrix to quantum circuitstring to bra-ket notationOptionsconvert methodoptionQC_TO_BRA_KETexpression{simplify, expand}, print{raw}QC_TO_MATRIXprint{raw}MATRIX_TO_QClabel{str}STR_TO_BRA_KETprint{raw}fromqiskit_class_converterimportConversionServiceConversionService(conversion_type="QC_TO_BRA_KET",option={"expression":"simplify"})Required dataMATRIX_TO_QCUser's QuantumCircuit objectfromqiskitimportQuantumCircuitfromqiskit_class_converterimportConversionServiceinput_value=[[1,0,0,0],[0,0,0,1],[0,0,1,0],[0,1,0,0]]sample_converter=ConversionService(conversion_type="MATRIX_TO_QC")result=sample_converter.convert(input_value=input_value)# using user's QuantumCircuit objectquantum_circuit=QuantumCircuit(2,2)quantum_circuit.append(result,[0,1])How to Installpipinstallqiskit-classroom-converterDocker Pull & RunAlternative installation with docker image.dockerpullghcr.io/kmu-quantum-classroom/qiskit-classroom-converter
dockerrun-p8888:8888ghcr.io/kmu-quantum-classroom/qiskit-classroom-converterDependenciesqiskitUsagefromqiskitimportQuantumCircuitfromqiskit_class_converterimportConversionService# quantum circuit to matrixquantum_circuit=QuantumCircuit(2,2)quantum_circuit.x(0)quantum_circuit.cx(0,1)sample_converter=ConversionService(conversion_type="QC_TO_MATRIX")result=sample_converter.convert(input_value=quantum_circuit)code :example.pyHow to test the softwarepython-munittest-vortoxARM PlatformMac ARM chips users may have issues running this package.We have provided a Dockerfile, which can be used docker-compose.docker-composeup--buildAcknowledgement국문 : "본 연구는 2022년 과학기술정보통신부 및 정보통신기획평가원의 SW중심대학사업의 연구결과로 수행되었음"(2022-0-00964)English : "This research was supported by the MIST(Ministry of Science, ICT), Korea, under the National Program for Excellence in SW), supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation) in 2022"(2022-0-00964)
Package: qiskit-dynamics
Qiskit DynamicsThis repo is still in the early stages of development, there will be breaking API changesQiskit Dynamics is an open-source project for building, transforming, and solving time-dependent
quantum systems in Qiskit.The goal of Qiskit Dynamics is to provide access to different numerical methods for solving
differential equations, and to automate common processes typically performed by hand, e.g. applying
frame transformations or rotating wave approximations to system and control Hamiltonians.Qiskit Dynamics can be configured to use eitherNumPyorJAXas the backend for array operations.NumPyis the default, andJAXis
an optional dependency.JAXprovides just-in-time compilation,
automatic differentiation, and GPU execution, and therefore is well-suited to tasks involving
repeated evaluation of functions with different parameters; E.g. simulating a model of a quantum
system over a range of parameter values, or optimizing the parameters of control sequence.Reference documentation may be foundhere, includingtutorials,user guide,API reference, andDiscussions.InstallationQiskit Dynamics may be installed using pip via:pip install qiskit-dynamicsAdditionally, Qiskit Dynamics may be installed simultaneously with the CPU version of
JAX via:pip install "qiskit-dynamics[jax]"Installing JAX with GPU support must be done manually, for instructions refer to theJAX installation guide.Contribution GuidelinesIf you'd like to contribute to Qiskit Dynamics, please take a look at ourcontribution guidelines. This project adheres to Qiskit'scode of conduct. By participating, you are expected to uphold this code.We useGitHub issuesfor tracking
requests and bugs. Pleasejoin the Qiskit Slack communityand use our#qiskit-dynamicschannel for discussion and simple
questions. For questions that are more suited for a forum we use the Qiskit tag in theStack Exchange.Authors and CitationQiskit Dynamics is the work ofmany peoplewho
contribute to the project at different levels. If you use Qiskit, please cite as per the includedBibTeX file.LicenseApache License 2.0
Package: qiskit-experiments
Qiskit ExperimentsQiskit Experimentsis a repository that builds tools for building, running,
and analyzing experiments on noisy quantum computers using Qiskit.To learn more about the package, you can see themost up-to-date documentationcorresponding to the main branch of this repository or thedocumentation for the latest stable release.Contribution GuidelinesIf you'd like to contribute to Qiskit Experiments, please take a look at ourcontribution guidelines. This project adheres to Qiskit'scode of conduct. By participating, you are expected to
uphold this code.We useGitHub issuesfor
tracking requests and bugs. Pleasejoin the Qiskit Slack communityand use the#experimentschannel for discussion and
simple questions.
For questions that are more suited for a forum we use the Qiskit tag inStack Exchange.Authors and CitationQiskit Experiments is the work ofmany peoplewho contribute
to the project at different levels. If you use Qiskit Experiments, please cite ourpaperas per the includedcitation file.LicenseApache License 2.0