The Useful Quantum Computing Techniques for Artificial Intelligence Engineers
Abstract
The hottest topics for many researchers in the past five years were Artificial Intelligence (AI) and machine learning. With many kinds of researches using machine learning, numerous AI engineers are still emerging. If the center of current research trends is on AI and machine learning, the center of near-future research trends will be on quantum computing techniques. The qubit implementation via superconductivity, diamond NitrogenVacancy (NV) center, ion-trap, and etc. has made quantum computers really exist. And cloud computing has made it possible for researchers around the world to use quantum computers remotely to their researches. The universalization of quantum computing techniques is no longer a story of the distant future, even more so for numerous AI engineers. This paper introduces some useful quantum computing techniques for AI engineers such as Quadratic Unconstrained Binary Optimization (QUBO), Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Harrow-Hassidim-Lloyd (HHL) algorithm.
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The most prominent problem formulations for CO on quantum computers are the Ising-model and the equivalent Quadratic Unconstrained Binary Optimization (QUBO) model.
“The most prominent problem formulations for CO on quantum computers are the Ising-model [189] and the equivalent Quadratic Unconstrained Binary Optimization (QUBO) model [53, 150, 173].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [173]
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- Authors
- Choi, J., Oh, S., & Kim, J.
- Affiliations
- Chung-Ang University (KR)
- Korea University (KR)
- Venue
- 2020 International Conference on Information Networking (ICOIN)
- Publisher
- IEEE
- Published
- 2020-01
- Pages
- 1-3
- ISSN
- 1976-7684 (ISSN-L)
- OpenAlex
- W3010468967
- Cited by
- 39
- References
- 25
- Citations by year
- 2025: 6 · 2024: 9 · 2023: 9 · 2022: 5 · 2021: 9 · 2020: 1
- FWCI
- 2.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Information Networking (publishedVersion)
- 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 computing, artificial intelligence, machine learning, superconducting qubits, nitrogen-vacancy centers, cloud quantum computing, QUBO, variational quantum eigensolver, quantum approximate optimization algorithm, Harrow-Hassidim-Lloyd algorithm, quantum machine learning
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