Qiskit

Qiskit
Name	Qiskit
Developer	IBM
Initial release	2017
Programming languages	Python, C++, Rust
Operating system	Cross-platform
License	Apache License 2.0

Qiskit is an open-source software development kit for working with quantum computers and simulators. It provides tools for designing quantum circuits, compiling to hardware backends, and running experiments on cloud-accessible quantum processors. Qiskit connects research institutions, industry partners, and educational programs with quantum hardware and software resources.

Overview

Qiskit interoperates with quantum processors and simulators from IBM Research, IBM Quantum, Rigetti Computing, Google Quantum AI, Intel, Microsoft, Amazon Web Services, and D-Wave via adapters and middleware. It supports algorithm development influenced by work at Massachusetts Institute of Technology, Stanford University, University of Oxford, University of Cambridge, California Institute of Technology, Harvard University, University of Waterloo, Universität Innsbruck, ETH Zurich, and University of Tokyo. The toolkit is used in collaborations with organizations such as CERN, NASA, Los Alamos National Laboratory, Argonne National Laboratory, Sandia National Laboratories, and Lawrence Berkeley National Laboratory. Qiskit integrates with platforms like GitHub, Docker, Kubernetes, Anaconda, Jupyter, Visual Studio Code, and PyCharm for workflow management.

History and Development

Development began at IBM Research and grew through partnerships with IBM Quantum and the IBM Q Experience initiative, drawing on milestones from the quantum computing roadmaps at Google, Microsoft, and Honeywell. Early releases coincided with demonstrations at events like the International Conference on Quantum Technologies, IEEE Quantum Week, and the ACM Symposium on Theory of Computing. Contributors include researchers and engineers formerly associated with Bell Labs, IBM Thomas J. Watson Research Center, IBM T.J. Watson, and collaborations with startups such as Zapata Computing, Xanadu, and Cambridge Quantum (now Quantinuum). Funding and institutional support have roots in programs by DARPA, the National Science Foundation, the European Research Council, Japan Science and Technology Agency, and the Canadian Institute for Advanced Research.

Architecture and Components

The architecture separates into layers comparable to models used by the Open Systems Interconnection work of ISO and stack approaches from IBM, Intel, and Google. Major components include Terra, Aer, Ignis, Aqua, and extensions developed in research labs like Los Alamos and institutions such as MIT Lincoln Laboratory. Terra handles circuit representation and transpilation similar in role to LLVM in compiler toolchains from Red Hat and GNU. Aer provides high-performance simulation inspired by HPC projects at Oak Ridge National Laboratory and Lawrence Livermore National Laboratory. Extensions and modules interoperate with libraries from NumPy, SciPy, Matplotlib, NetworkX, Pandas, SymPy, Scikit-learn, PyTorch, TensorFlow, and CUDA toolkits supported by NVIDIA and AMD. Hardware backends use OpenPulse concepts and interfaces akin to those in projects at Fermilab and CERN, and work with control electronics developed by Keysight, Tektronix, Zurich Instruments, and Quantum Machines.

Programming Model and Languages

Qiskit exposes a Python-first API while supporting lower-level control through C++, Rust, and integration with languages from projects at Google (Cirq), Microsoft (Q#), Rigetti (PyQuil), and Xanadu (PennyLane). The circuit model aligns with theoretical foundations from Richard Feynman, Peter Shor, and David Deutsch, and practical algorithms reference work by Lov Grover, Alexei Kitaev, Ignacio Cirac, and John Preskill. Quantum information concepts draw on textbooks and research by Michael Nielsen, Isaac Chuang, John Preskill, Scott Aaronson, and Lovasz. The SDK enables hybrid quantum-classical workflows used in collaborations with companies such as Goldman Sachs, JPMorgan Chase, ExxonMobil, Pfizer, BASF, Volkswagen, Daimler, Bosch, and Siemens.

Use Cases and Applications

Qiskit is applied in quantum chemistry studies referencing methods from Walter Kohn and John Pople, materials science projects tied to work at Max Planck Society and Fraunhofer Society, optimization tasks inspired by operations research at INFORMS and the RAND Corporation, and machine learning experiments following research at DeepMind, OpenAI, Facebook AI Research, and Google Brain. Industry pilots cover portfolio optimization at BlackRock, risk modeling at Morgan Stanley, drug discovery at Roche and Novartis, and logistics at UPS and DHL. Academic applications span quantum error correction studies in collaborations with MIT, Caltech, Princeton University, University of Chicago, Columbia University, and University of California, Berkeley.

Community, Governance, and Ecosystem

The project governance model involves contributors from IBM, research universities, consortiums such as the Quantum Economic Development Consortium, Q-NEXT, the Quantum Flagship, and standardization bodies including ISO and IEEE Quantum. Community events include workshops at NeurIPS, ICML, QIP, APS March Meeting, ECS, and ICLR. The ecosystem features partnerships with cloud providers Amazon Web Services, Microsoft Azure, Google Cloud Platform, and academic initiatives at Oxford, Cambridge, ETH Zurich, and Tsinghua University. Training and outreach draw on curricula from edX, Coursera, Udacity, and university extension programs at Stanford Online and MITx.

Category:Quantum computing software