Quantum Optimization and Quantum Learning: A Survey
Open accessAbstract
Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing.
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Quantum-inspired algorithms are designed to be executed on classical rather than quantum hardware and, thus, are out of scope of this mapping study.
“However, such algorithms are designed to be executed on classical rather than quantum hardware and, thus, are out of scope of this mapping study.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [198]
The Introduction (page unavailable) says these algorithms draw on or are inspired by quantum mechanisms and are evaluated by simulating quantum computing rather than testing on a real quantum computer. It attributes that practice to the current limitations of quantum hardware; it does not say quantum-inspired algorithms are designed to run on classical hardware instead of quantum hardware. The mapping study’s scope decision is the citing authors’ own. (LLM, conf=0.84)
Experimental — accuracy not yet measuredQuantum-inspired algorithms have been covered by Li et al. [198].
“Quantum-inspired algorithms, also known as quantum intelligent algorithms, have been covered by Li et al. [198].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [198]
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References 206 · Cited by 177
Research works printed but not linked: 8
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- Authors
- Li, Y., Tian, M., Liu, G., Peng, C., & Jiao, L.
- Affiliations
- Xidian University (CN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 23568-23593
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3003257958
- Cited by
- 177
- References
- 206
- Citations by year
- 2026: 29 · 2025: 52 · 2024: 30 · 2023: 31 · 2022: 17 · 2021: 17 · 2020: 1
- FWCI
- 6.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61772399, U1701267, 61773304, 61672405, 61772400)
- Key Research and Development Program in Shaanxi Province of China (2019ZDLGY09-05)
- Program for Cheung Kong Scholars and Innovative Research Team in University (IRT_15R53)
- Technology Foundation for Selected Overseas Chinese Scholar in Shaanxi (2017021, 2018021)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum optimization, quantum machine learning, quantum algorithms, quantum computing, quantum-inspired algorithms, artificial intelligence, quantum computing simulation
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