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Optimization of Quantum Computing Models Inspired by D-Wave Quantum Annealing
Tsinghua Science and Technology, 2020 With the slow progress of universal quantum computers, studies on the feasibility of optimization by a dedicated and quantum-annealing-based annealer are important.
Baonan Wang, Feng Hu, Chao Wang
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A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum support ...
Amer Delilbasic +4 more
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A quantum annealing approach to solve max-cover problem
Tạp chí Khoa học, 2022 The Max-Cover is a NP-hard problem. Therefore, the heuristic approach is suitable to solve this problem for large instances. Quantum annealing is a heuristic quantum optimization algorithm that can be used to handle the Max-Cover problem.
NGUYEN Thi Uyen +3 more
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Reverse annealing for nonnegative/binary matrix factorization.
PLoS ONE, 2021 It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly ...
John Golden, Daniel O'Malley
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The quantum annealing gap and quench dynamics in the exact cover problem [PDF]
Quantum, 2022 Quenching and annealing are extreme opposites in the time evolution of a quantum system: Annealing explores equilibrium phases of a Hamiltonian with slowly changing parameters and can be exploited as a tool for solving complex optimization problems.
Bernhard Irsigler, Tobias Grass
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IEEE Access, 2023 Simulated Quantum Annealing (SQA) is a heuristic algorithm which can solve Quadratic Unconstrained Binary Optimization (QUBO) problems by emulating the exploration of the solution space done by a quantum annealer.
Deborah Volpe +3 more
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Mean field approximation for solving QUBO problems.
PLoS ONE, 2022 The Quadratic Unconstrained Binary Optimization (QUBO) problem is NP-hard. Some exact methods like the Branch-and-Bound algorithm are suitable for small problems.
Máté Tibor Veszeli, Gábor Vattay
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An Overview of Approaches to Modernize Quantum Annealing Using Local Searches [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 I describe how real quantum annealers may be used to perform local (in state space) searches around specified states, rather than the global searches traditionally implemented in the quantum annealing algorithm.
Nicholas Chancellor
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PLoS ONE, 2022 Quantum annealing has gained considerable attention because it can be applied to combinatorial optimization problems, which have numerous applications in logistics, scheduling, and finance.
Michiya Kuramata +2 more
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Scientific Reports, 2022 Quantum annealing is a way to solve a combinational optimization problem where quantum fluctuation is induced by transverse fields. Recently, a bifurcation-based quantum annealing with spin-1 particles was suggested as another mechanism to implement the ...
Yuichiro Matsuzaki +2 more
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