Results 31 to 40 of about 118,204 (269)
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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Approximate approximation on a quantum annealer [PDF]
Proceedings of the 17th ACM International Conference on Computing Frontiers, 2020 Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical devices that aim at this class of problems by exploiting quantum mechanical properties of nature.
Irmi Sax +5 more
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An introduction to quantum annealing [PDF]
RAIRO - Theoretical Informatics and Applications, 2011 Quantum Annealing, or Quantum Stochastic Optimization, is a classical randomized algorithm which provides good heuristics for the solution of hard optimization problems. The algorithm, suggested by the behaviour of quantum systems, is an example of proficuous cross contamination between classical and quantum computer science.
D. de Falco, D. Tamascelli
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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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Molecular dynamics on quantum annealers
Scientific Reports, 2022 AbstractIn this work we demonstrate a practical prospect of using quantum annealers for simulation of molecular dynamics. A methodology developed for this goal, dubbed Quantum Differential Equations (QDE), is applied to propagate classical trajectories for the vibration of the hydrogen molecule in several regimes: nearly harmonic, highly anharmonic ...
Gaidai, Igor +7 more
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Quantum Annealing with the Jarzynski Equality [PDF]
Physical Review Letters, 2010 We show a practical application of the Jarzynski equality in quantum computation. Its implementation may open a way to solve combinatorial optimization problems, minimization of a real single-valued function, cost function, with many arguments. We consider to incorpolate the Jarzynski equality into quantum annealing, which is one of the generic ...
Masayuki Ohzeki, Hidetoshi Nishimori
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Learning quantum annealing [PDF]
Quantum Information and Computation, 2017 We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate generalizability to larger systems. The amount of additional learning necessary decreases as the size of the system increases.
Behrman, Elizabeth C. +2 more
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Single-Qubit Fidelity Assessment of Quantum Annealing Hardware
IEEE Transactions on Quantum Engineering, 2021 As a wide variety of quantum computing platforms become available, methods for assessing and comparing the performance of these devices are of increasing interest and importance.
Jon Nelson +3 more
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Quantum Annealing for Clustering [PDF]
CoRR, 2009 8 pages, 6 figures, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009 ...
Kenichi Kurihara +2 more
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Quantum annealing for combinatorial clustering [PDF]
Quantum Information Processing, 2018 Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points.
Vaibhaw Kumar +3 more
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