Explore projects and hardware providers that use the open-source Qiskit toolkit or are built on top of it.

Compile and optimize your quantum circuits

A doubly stochastic matrices-based approach to optimal qubit routing.

A Qiskit transpiler stage plugin that reuses qubits through mid-circuit measurement and reset.

Transpiler routing method that uses the Time-Optimal Qubit Mapping (TOQM) algorithm.

Solve the routing and layout problems as a binary integer programming (BIP) problem. This is an implementation of G. Nannicini et al. "Optimal qubit assignment and routing via integer programming."

MIRAGE is a transpilation plugin for quantum circuits that minimizes the use of SWAP gates while optimizing native basis gate decomposition through mirror gates. Specifically designed for iSWAP-based quantum systems, MIRAGE improves circuit depth, making quantum algorithms more practical and efficient.

Access IBM Quantum and 3rd party systems and simulators to the following providers

Run Qiskit primitives on IBM Quantum hardware with built-in error suppression and mitigation.

Access IBM Quantum systems.

This project contains a provider that allows access to IonQ ion trap quantum systems.

Qiskit provider for ion-trap quantum computers from Alpine Quantum Technologies (AQT).

Qiskit-cold-atom builds on core Qiskit functionalities to integrate programmable quantum simulators of trapped cold atoms in a gate- and circuit-based framework. The project includes a provider and simulators for fermionic and spin-based systems.

Rigetti Provider for Qiskit.

Qiskit-Qulacs allows users to execute Qiskit programs using Qulacs backend.

This package is used to access SuperstaQ via a Web API through Qiskit. Qiskit programmers can take advantage of the applications, pulse level optimizations, and write-once-target-all features of SuperstaQ with this package.

Packages that help apply quantum technology to real-world use cases

A PyTorch-centric hybrid classical-quantum dynamic neural networks framework.

Use flexible building blocks, such as quantum kernels and neural networks, to create quantum machine learning algorithms.

Model and solve problems in physics, chemistry, material science, and biology by using quantum simulations.

This framework includes uncertainty components for stock/securities problems, Ising translators for portfolio optimizations, and data providers to source real or random data for finance experiments.

Model optimization problems, convert them between different representations, and solve them by using quantum optimization algorithms on simulators or systems.

Build, transform, and solve time-dependent quantum systems.

A library of quantum algorithms for near-term quantum devices with short-depth circuits.

NQI C2QA project to simulate hybrid boson-qubit systems within Qiskit.

Train and use quantum kernels for machine learning. This toolkit is for researchers and practitioners looking to explore and apply these algorithms to their work.

Simulate larger chemical and physical systems using a Variational Quantum Eigensolver. Entanglement Forging doubles the size of the system that can be exactly simulated on a fixed set of qubits.

Solve larger combinatorial optimization problems with quantum random access codes. Save resources by encoding many binary variables on a single qubit before solving with standard variational algorithms.

A library for supervised machine learning based on quantum computing and Riemannian geometry. The project is built on top of the Qiskit and pyRiemann projects and focuses on the classification of time series data.

A third-party integration plugin of Qiskit Nature and PySCF.

QiskitOpt.jl is a Julia package that exports a JuMP wrapper for qiskit-optimization.

This repository contains the latest prototype implementation of the Qiskit Nature + PySCF DFT Embedding. It is based on "Quantum HF/DFT-embedding algorithms for electronic structure calculations: Scaling up to complex molecular systems".

The Variational Quantum Linear Solver (VQLS) uses an optimization approach to solve linear systems of equations. The vqls-prototype allows to easily setup and deploy a VQLS instance on different backends through the use of qiskit primitives and the runtime library.

Apply orbital optimization to quantum algorithms that solve electronic structure Hamiltonians. This collection of methods optimize the underlying basis set through orbital rotations, finding more accurate energies with fewer qubits.

Cool projects that extend, interface with, or use Qiskit

An imperative programming language for near-term quantum computing. Describe quantum programs by using the measurement-based quantum circuit model with support for classical feedforward flow control based on measurement outcomes.

High-performance quantum computing simulators with realistic noise models.

A quantum computing SDK

Mitiq is a Python toolkit for implementing error mitigation techniques on quantum computers.

Design superconducting qubits through a Python API or a visual interface.

Qiskit Metal E&M analysis with Ansys and the energy-participation-ratio method is based on pyEPR.

The PennyLane-Qiskit plugin integrates the Qiskit quantum computing framework with PennyLane's quantum machine learning capabilities.

Run characterizing, calibrating, and benchmarking experiments.

RasQberry is a functional model of IBM Quantum System One, and can run Qiskit on the integrated Raspberry Pi

A quantum version of the classic game Pong built with Qiskit and PyGame

Q-CTRL Open Controls is an open-source Python package that makes it easy to create and deploy established error-robust quantum control protocols from the open literature.

Qiskit Topological Codes.

This platform allows you to execute quantum algorithms using the cQASM language.

The C3 package is intended to close the loop between open-loop control optimization, control pulse calibration, and model-matching based on calibration data.

Run quantum computing research experiments by using Qiskit and IBM Quantum services, demonstrating best practices by example.

Decompose large circuits into smaller, hardware-executable circuits, then use their results to reconstruct the original circuit's outcome. This toolbox includes entanglement forging, circuit knitting, and classical embedding.

For quantum error correction developers, experimentalists, and theorists.

The qBraid-SDK is a Python toolkit for cross-framework abstraction, transpilation, and execution of quantum programs.

Execute Qiskit programs as long-running jobs and distribute them across many CPUs, GPUs, and QPUs.

A template repository for generating new quantum prototypes based on Qiskit.

Matrix-free Measurement Mitigation (M3). Reduce the effects of readout errors from noisy quantum hardware.

A discord bot that allows you to execute Quantum Circuits, look up the Qiskit's Documentation, and search questions on the Quantum Computing StackExchange

quantumcat is a platform-independent, open-source, high-level quantum computing library, which allows the quantum community to focus on developing platform-independent quantum applications without much effort.

Qlasskit is a Python library that allows quantum developers to write classical algorithms in pure Python and translate them into quantum circuits.

Kaleidoscope

Easy-to-use Python package designed to enable symbolic quantum computation in Qiskit. It provides the basic tools for the symbolic evaluation of statevectors, density matrices, and unitary operators directly created from parametric Qiskit quantum circuits. The implementation is based on the Sympy library as backend for symbolic expressions manipulation.

Spinoza is a quantum state simulator (implemented in Rust) that is one of the fastest open-source simulators. Spinoza is implemented using a functional approach. Additionally, Spinoza has a QuantumCircuit object-oriented interface, which partially matches Qiskit's interface. Spinoza is capable of running in a myriad of computing environments (e.g., small workstations), and on various architectures.... At this juncture, Spinoza only utilizes a single thread; however, it is designed to be easily extended into a parallel version, as well as a distributed version. The paper associated with Spinoza is available at arXiv:2303.01493.

Visualise the effects of single-qubit gates on a qubit via Bloch sphere simulation in a Tkinter software.

A Python package that uses a backend written in Julia to implement high performance features for standard Qiskit.

What would happen if you combine Tetris with a Quantum computer? The winning entry of the Quantum Design Jam from IBM and Parsons in October 2021 explores just that!

An extension to Pytket (a python module for interfacing with CQC tket) that allows Pytket circuits to be run on IBM backends and simulators, as well as conversion to and from Qiskit representations.

A Python-Qiskit-based package that provides capabilities of easily generating, executing and analyzing quantum circuits for satisfiability problems according to user-defined constraints. The circuits generated by the program are based on Grover's algorithm and its amplitude-amplification generalization.

A lightweight framework to enable configurable memory consumption when simulating large quantum circuits.

Project is aimed to create simple general interface to track quantum experiments, store and search them in an easy way.

Distributed quantum computing is a concept that proposes to connect multiple quantum computers in a network to leverage a collection of more, but physically separated, qubits. In order to perform distributed quantum computing, it is necessary to add the addition of classical communication and entanglement distribution so that the control information from one qubit can be applied to another that is... located on another quantum computer. For more details on distributed quantum computing, see the Medium blog post "Distributed Quantum Computing: A path to large scale quantum computing". In this project, we aim to validate distributed quantum algorithms using Qiskit. Because Qiskit does not yet come with networking features, we embed a "virtual network topology" into large circuits to mimic distributed quantum computing. The idea is to take a monolithic quantum circuit developed in the Qiskit language and distribute the circuit according to an artificially segmented version of a quantum processor. The inputs to the library are a quantum algorithm written monolithically (i.e., in a single circuit) and a topology parameter that represents the artificial segmentation of the single quantum processor. The algorithm takes these two inputs and remaps the Qiskit circuit to the specified segmentation, adding all necessary steps to perform an equivalent distributed quantum circuit. Our algorithm for achieving this is based on the work "Distributed Quantum Computing and Network Control for Accelerated VQE" (doi: 10.1109/TQE.2021.3057908). The algorithm output is another Qiskit circuit with the equivalent measurement statistics but with all of the additional logic needed to perform a distributed version.

Implements expectation value measurements in Qiskit using optimal dense grouping. Dense-ev provides an improvement of ~2^m over naive grouping and (3/2)^m over qubit-wise commuting groups.

Code based on the paper "Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning".

Convert quantum circuits, matrices, and bra-ket strings. This converter includes the following conversion functions: quantum circuit to bra-ket notation, quantum circuit to matrix, matrix to quantum circuit, bra-ket notation to matrix.

Qiskit-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 supported : Quantum circuit to Dirac notation, quantum circuit to matrix, matrix to quantum circuit etc.

Write quantum programs as Python functions instead of circuit objects. Create higher-level quantum data types and return measurement results as bool-like objects.

GitHub Codespace template repository based on Zoose Quantum, a custom Docker image with everything included, so you can be up and running with any of the major quantum libraries (incl. Qiskit) with only two clicks! No installation required. Ideal for beginners or people who want to code quantum circuits on the go. Code quantum circuits straight in your browser with VSCode.