Machine learning meets quantum foundations: A brief survey
Open accessAbstract
The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seek to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, the authors compile the representative works done so far at the interface of machine learning and quantum foundations. The authors conclude the survey with potential future directions.
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Investigating the potential of NISQ algorithms using ideas from quantum foundations such as contextuality and entanglement is helpful to understand the boundary where quantum computers provide an advantage.
“Investigating the potential of NISQ algorithms using ideas from quantum foundations such as contextuality and entanglement are helpful in that respect (Bharti et al. , 2020; Deutsch, 2020).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Bharti et al., 2020
The paper discusses entanglement and contextuality as topics in quantum foundations (Section III, p. 5) and describes a variational algorithm for studying Bell-inequality violations on near-term quantum computers (Section V, p. 10). It does not say that this line of investigation helps identify the boundary at which quantum computers gain an advantage over classical computers. (LLM, conf=0.83)
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References 179 · Cited by 59
Research works printed but not linked: 5
- [1]Shai Shalev-Shwartz and Shai Ben-David. Understanding machine learning: From theory to algorithms . Cambridge university press, 2014.
- [2]Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning . MIT press, 2016.
- [3]Mohammed AlQuraishi. Alphafold at casp13. Bioinformatics , 35(22):4862-4865, 2019.
- [4]James P Bridge, Sean B Holden, and Lawrence C Paulson. Machine learning for first-order theorem proving. Journal of automated reasoning , 53(2):141-172, 2014.
- [5]Antonio Lavecchia. Machine-learning approaches in drug discovery: methods and applications. Drug discovery today , 20(3):318-331, 2015. cality, it is genuine to ask if the methods could be useful to solve problems in quantum steering and contextuality. Recently, ideas from the exclusivity graph approach to contextuality were used to investigate problems involving causal inference [164]. Ideas from quantum foundations could further assist in developing a deeper understanding of machine learning or in general artificial intelligence.
- Authors
- Bharti, K., Haug, T., Vedral, V., & Kwek, L.-C.
- Affiliations
- Centre for Quantum Technologies (SG)
- Venue
- AVS Quantum Science
- Publisher
- American Vacuum Society
- Published
- 2020-07-10
- Volume
- 2
- Issue
- 3
- ISSN
- 2639-0213 (ISSN-L)
- OpenAlex
- W3013999312
- Cited by
- 59
- References
- 179
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 4 · 2023: 11 · 2022: 16 · 2021: 11 · 2020: 4
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- AVS Quantum Science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- machine learning, quantum foundations, quantum mechanics, conceptual foundations of quantum mechanics
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