Artificial intelligence for quantum computing
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.
Authors
- Sam Stanwyck
- Alberto Peruzzo (ORCID: https://orcid.org/0000-0002-7953-3875)
- M.E. Wolf (ORCID: https://orcid.org/0000-0003-0690-6789)
- Timothy Costa (ORCID: https://orcid.org/0000-0003-4485-5900)
- Yuri Alexeev (ORCID: https://orcid.org/0000-0001-5066-2254)
- Simon C. Benjamin (ORCID: https://orcid.org/0000-0002-7766-5348)
- Tom Lubowe (ORCID: https://orcid.org/0009-0000-3325-7281)
- Natalia Ares (ORCID: https://orcid.org/0000-0003-2588-6322)
- Nicholas Harrigan (ORCID: https://orcid.org/0000-0001-5092-1180)
- Ikko Hamamura (ORCID: https://orcid.org/0000-0001-6416-5515)
- Kouhei Nakaji
- Federico Fedele (ORCID: https://orcid.org/0000-0003-4466-5576)
- Christopher Chamberland (ORCID: https://orcid.org/0000-0003-3239-5783)
- Taylor L. Patti (ORCID: https://orcid.org/0000-0002-4242-6072)
- Zhenyu Cai (ORCID: https://orcid.org/0000-0001-5659-4301)
- Marwa Farag (ORCID: https://orcid.org/0000-0003-4577-3384)
- Justin G. Lietz (ORCID: https://orcid.org/0000-0002-8398-5524)
- Bruno Schmitt
- Pooja Rao
- Elica Kyoseva (ORCID: https://orcid.org/0000-0002-9154-0293)
- Norm M. Tubman (ORCID: https://orcid.org/0000-0002-9577-8485)
- Alán Aspuru‐Guzik (ORCID: https://orcid.org/0000-0002-8277-4434)
- Shuxiang Cao (ORCID: https://orcid.org/0000-0001-7178-4250)
- Roger G. Melko (ORCID: https://orcid.org/0000-0002-5505-8176)
- Jin-Sung Kim (ORCID: https://orcid.org/0000-0003-2892-8022)
- Alexander McCaskey
- Zohim Chandani
- Hanrui Wang (ORCID: https://orcid.org/0009-0005-0498-2712)
Institutions
- Ames Research Center (US)
- University of Waterloo (CA)
- University of California, Los Angeles (US)
- Perimeter Institute (CA)
- University of Toronto (CA)
- University of Oxford (GB)
- Quantum Motion Technologies (United Kingdom) (GB)
- Quandela (France) (FR)
- Vector Institute (CA)
- Nvidia (United States) (US)
- Science Oxford (GB)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1038/s41467-025-65836-3
- Citations
- 8
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 19.85
Funders
- U.S. Department of Energy
- Innovation, Science and Economic Development Canada
- UK Research and Innovation
- Government of Canada
- European Commission
- Ministero dello Sviluppo Economico
- Institut Périmètre de physique théorique
- Office of Science
- Natural Resources Canada
- Engineering and Physical Sciences Research Council
- Research Executive Agency