Artificial intelligence and machine learning for quantum technologies
Open accessAbstract
In this Perspective, the authors review how machine learning, and more broadly methods of artificial intelligence, are utilized in advancing quantum technologies, specifically the design, control, calibration and optimization of quantum devices. They also discuss open challenges in the field and potential future directions within the next decade.
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To achieve sufficient reliability to enable sustained quantum computation, iterative error mitigation is required.
“To achieve sufficient reliability to enable sustained quantum computation, iterative error mitigation is required [36].”
From Quantum Computing: Vision and Challenges (2025) · cites this paper as [36]
The 'Quantum error correction' section says correcting errors is indispensable because real devices cannot run error-free calculations. It does not say that iterative error mitigation specifically is required for sustained computation; the 'Code search' section also describes autonomous correction without active feedback. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe invention of quantum software is an emerging yet relatively less developed field compared to quantum modelling and quantum technology.
“The invention of quantum software is an emerging yet relatively less developed field compared to quantum modelling and quantum technology [36].”
From Quantum Computing: Vision and Challenges (2025) · cites this paper as [36]
In “Discovering new algorithms” (page not provided), the paper presents autonomous discovery of quantum programs as a future possibility, while “Discovery of quantum experiments” describes approaches already in use. This supports the idea that quantum-program invention is emerging, but the paper does not compare its development with quantum modelling and quantum technology as the claim does. (LLM, conf=0.82)
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References 156 · Cited by 162
Research works printed but not linked: 17
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- Authors
- Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F.
- Affiliations
- Max Planck Institute for the Science of Light (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 2023-01-03
- Volume
- 107
- Issue
- 1
- ISSN
- 0556-2791 (ISSN-L)
- 2469-9926
- 2469-9934
- OpenAlex
- W4313462484
- Cited by
- 162
- References
- 156
- Citations by year
- 2026: 39 · 2025: 61 · 2024: 41 · 2023: 21
- FWCI
- 15.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review A (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Munich Quantum Valley
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, machine learning, quantum technologies, quantum device design, quantum control, quantum optimization, open problems
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