QuantumKatas - Programming exercises for learning Q# and quantum computing

•    TeX

The Quantum Katas are a series of self-paced tutorials aimed at teaching you elements of quantum computing and Q# programming at the same time. To get started with the Quantum Katas, you'll first need to install the Quantum Development Kit, available for Windows 10, macOS, and for Linux. Please see the install guide for the Quantum Development Kit if you do not already have the Quantum Development Kit installed.

qiskit-terra - Terra provides the foundations for Qiskit

•    Python

The Quantum Information Science Kit (Qiskit for short) is a software development kit (SDK) for working with OpenQASM and the IBM Q Experience (QX). Use Qiskit to create quantum computing programs, compile them, and execute them on one of several backends (online Real quantum processors, online simulators, and local simulators). For the online backends, Qiskit uses our python API client to connect to the IBM Q Experience.

awesome-quantum-machine-learning - Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

•    HTML

A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).

Cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits

•    Python

Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. Follow these instructions.

pyquil - A Python library for quantum programming using Quil.

•    Python

Please Note: PyQuil, along with quilc, the QVM, and other libraries, make up what is called the Forest SDK. To make full use of pyQuil's functionality, you will need to additionally have installed quilc and the QVM. This can be done by following their respective READMEs, or by downloading them as binaries from here. PyQuil can be installed using conda, pip, or directly from source.

qiskit - Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules

•    OpenQASM

Qiskit is an open-source SDK for working with quantum computers at the level of circuits, algorithms, and application modules. Qiskit is made up of elements that work together to enable quantum computing. This is a simple meta-package to install the elements of Qiskit altogether.

qiskit-tutorials - A collection of Jupyter notebooks showing how to use the Qiskit SDK

•    Jupyter

In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use Qiskit for writing quantum computing programs, and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors are the IBM Quantum systems. For our community-contributed tutorials, please check out the qiskit-community-tutorials repository.

pyquil - A Python library for quantum programming using Quil.

•    Python

A library for easily generating Quil programs to be executed using the Rigetti Forest platform. pyQuil is licensed under the Apache 2.0 license. pyQuil can be used to build and manipulate Quil programs without restriction. However, to run programs (e.g., to get wavefunctions, get multishot experiment data), you will need an API key for Rigetti Forest. This will allow you to run your programs on the Rigetti Quantum Virtual Machine (QVM) or on a real quantum processor (QPU).

QuSimPy - A Multi-Qubit Ideal Quantum Computer Simulator

•    Python

Largely based on the code from corbett/QuantumComputing.

OpenFermion - The electronic structure package for quantum computers.

•    Python

OpenFermion is an open source library for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, this version features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. For more information, see our release paper. Installing OpenFermion requires pip. Make sure that you are using an up-to-date version of it. For information about getting started beyond what is provided below please see our tutorial in the examples folder as well as our detailed code documentation.

qiskit-api-py - A Python library for the Quantum Experience API

•    Python

The official API Client to use IBM Quantum Experience in Python. This package can be used in Jupyter Notebook.

qiskit-tutorial - A collection of Jupyter notebooks using Qiskit

•    Jupyter

In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use the QISKit for writing quantum computing programs and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors connects to the IBM Q devices. Please refer to this installation for installing and setting up QISKit and tutorials on your own machine.

QuantumComputing - This is an implementation of IBM's Quantum Experience in simulation; a 5-qubit quantum computer with a limited set of gates

•    Python

This is an implementation of IBM's Quantum Experience in simulation; a 5-qubit quantum computer with a limited set of gates "the world’s first quantum computing platform delivered via the IBM Cloud". Their implementation is available at http://www.research.ibm.com/quantum/. It is much easier to dig into the internals of how the quantum computer computes by seeing and tracing the linear algebra representation of gates and states and their interactions as desired–for IBM's examples or for one's own code.

openqasm - Gate and operation specification for quantum circuits

•    TeX

Specs, examples and tools for the OpenQASM intermediate representation. The latest version is: 2.0.

qcgpu-rust - A High Performance, Hardware accelerated, Quantum computer simulator in Rust

•    Rust

The goal of QCGPU is to provide a library for the simulation of quantum computers that is fast, efficient and portable. QCGPU is written in Rust and uses OpenCL to run code on the CPU, GPU or any other OpenCL supported devices. This library is meant to be used both independently and alongside established tools for example compilers or more general and high level frameworks. If you are interested in using QCGPU with IBM's QISKit framework or QISKit ACQUA, please see the repository qiskit-addon-qcgpu.

qmasm - Quantum macro assembler for D-Wave systems

•    Python

QMASM fills a gap in the software ecosystem for D-Wave's adiabatic quantum computers by shielding the programmer from having to know system-specific hardware details while still enabling programs to be expressed at a fairly low level of abstraction. It is therefore analogous to a conventional macro assembler and can be used in much the same way: as a target either for programmers who want a great deal of control over the hardware or for compilers that implement higher-level languages. N.B. This tool used to be called "QASM" but was renamed to avoid confusion with MIT's QASM, which is used to describe quantum circuits (a different model of quantum computation from what the D-Wave uses) and the IBM Quantum Experience's QASM (now OpenQASM) language, also used for describing quantum circuits.

grove - Quantum algorithms built using pyQuil.

•    Python

A collection of quantum algorithms built using the Rigetti Forest platform. Grove is licensed under the Apache 2.0 license. Features in the alpha package are considered experimental.

qsim - Schrödinger and Schrödinger-Feynman simulators for quantum circuits.

•    C++

Quantum circuit simulators qsim and qsimh. These simulators were used for cross entropy benchmarking in [1]. [1], F. Arute et al, "Quantum Supremacy Using a Programmable Superconducting Processor", Nature 574, 505, (2019).

learnquantum - Repo of resources to help learn about quantum computing.

This repository is for educational resources related to Quantum Computing. I hope this can serve as a living document and grow as the field matures and a clearer pedagogy emerges. Please contribute if you have resources you think would be valuable to others learning about this field.

qsel - Quantum programming language putting entanglement and superposition front and center

•    Python

What makes quantum computers more powerful than classical computers? Ask any expert, or watch any public lecture on the subject and the answer is clear. Entanglement and Superposition are what give quantum computers their power over classical computers. If these two ideas are at the heart of the power of quantum computing, it therefore makes sense that any programing language for quantum computing should make these two ideas front and center. This is where Quantum Super Entangled Language (qsel) comes in. It is a quantum programming language made entirely from superposition and entanglement. where your measurement results may be different.