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Quantum programming languages

Abstract

Quantum programming languages are essential to translate ideas into instructions that can be executed by a quantum computer. Not only are they crucial to the programming of quantum computers at scale but also they can facilitate the discovery and development of quantum algorithms even before hardware exists that is capable of executing them. Quantum programming languages are used for controlling existing physical devices, for estimating the execution costs of quantum algorithms on future devices, for teaching quantum computing concepts, or for verifying quantum algorithms and their implementations. They are used by newcomers and seasoned practitioners, researchers and developers working on the next ground-breaking discovery or applying known concepts to real-world problems. This variety in purpose and target audiences is reflected in the design and ecosystem of the existing quantum programming languages, depending on which factors a language prioritizes. In this Review, we highlight important aspects of quantum programming and how it differs from conventional programming. We overview a selection of several state-of-the-art quantum programming languages, highlight their salient features, and provide code samples for each of the languages and Docker files to facilitate installation of the software packages.

Key points

  • Quantum computing fundamentally differs from other means of computing, requiring the development of domain-specific programming languages and compilation techniques.

  • Quantum programming languages today cater to a variety of purposes and target audiences ranging from newcomers to seasoned practitioners, and which factors are prioritized substantially impacts the design of the language, the ecosystem and the community around it.

  • Programming languages and software tools facilitate the discovery, advancement and development of quantum applications by enabling the verification, resources estimation, program analysis and visualization of quantum applications; they are essential for understanding and analysing both large-scale applications and algorithms for near-term hardware and suitable circuit compilation methods.

  • The requirements for software tools for near-term devices and applications differ substantially from those geared towards scalable, fault-tolerant quantum computing.

  • The field of quantum programming languages and compilers is still nascent, and current research and implementation efforts are focused on low-level circuit optimizations, concentrating on the purely quantum pieces rather than on optimizations that act across the entire program structure.

  • Emulating and accelerating a similar progression to that followed by conventional computing over the past 50 years requires a collaborative effort across disciplines to take full advantage of the acquired knowledge.

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Acknowledgements

The authors thank P. Selinger, J. Ross, A. Javadi-Abhari and M. Martonosi for their input and advice. The authors thank V. Kliuchnikov for discussions and for sharing his perspectives.

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B.H., M.S., S.M. and C.G. researched data for the article. B.H., M.S., M.R., M.T. and K.S. contributed substantially to the discussion of the content. B.H., M.S., S.M., C.G. and A.G. wrote the manuscript. B.H., M.R., A.G. and K.S. reviewed and edited the manuscript before submission.

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Correspondence to Bettina Heim.

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Supplementary information

Glossary

Kraus operators

Operators defined as part of a theorem characterizing the action of completely positive maps.

Toffoli simulator

A simulator capable of simulating the execution of Toffoli gates.

Grover search

Quantum algorithm for searching an unstructured database.

Quantum phase estimation

Quantum algorithm for estimating the eigenphases of an operator.

Side effects

A side effect in programming is an effect that modifies the program state outside the local environment.

Monad

In programming, a monad is a structure that represents computations.

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Heim, B., Soeken, M., Marshall, S. et al. Quantum programming languages. Nat Rev Phys 2, 709–722 (2020). https://doi.org/10.1038/s42254-020-00245-7

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