4.7 Article

Parallel quantum annealing

期刊

SCIENTIFIC REPORTS
卷 12, 期 1, 页码 -

出版社

NATURE PORTFOLIO
DOI: 10.1038/s41598-022-08394-8

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资金

  1. Laboratory Directed Research and Development program of Los Alamos National Laboratory [20190065DR, BG05M2OP001-1.001-0003]
  2. Science and Education for Smart Growth Operational Program - European Union through the European Structural and Investment Funds

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This article introduces the quantum annealers of D-Wave Systems, Inc. and discusses a method called parallel quantum annealing to improve computational efficiency. Parallel processing with quantum annealing can significantly accelerate the time-to-solution, and even solving a single problem using parallel quantum annealing can reduce the solving time.
Quantum annealers of D-Wave Systems, Inc., offer an efficient way to compute high quality solutions of NP-hard problems. This is done by mapping a problem onto the physical qubits of the quantum chip, from which a solution is obtained after quantum annealing. However, since the connectivity of the physical qubits on the chip is limited, a minor embedding of the problem structure onto the chip is required. In this process, and especially for smaller problems, many qubits will stay unused. We propose a novel method, called parallel quantum annealing, to make better use of available qubits, wherein either the same or several independent problems are solved in the same annealing cycle of a quantum annealer, assuming enough physical qubits are available to embed more than one problem. Although the individual solution quality may be slightly decreased when solving several problems in parallel (as opposed to solving each problem separately), we demonstrate that our method may give dramatic speed-ups in terms of the Time-To-Solution (TTS) metric for solving instances of the Maximum Clique problem when compared to solving each problem sequentially on the quantum annealer. Additionally, we show that solving a single Maximum Clique problem using parallel quantum annealing reduces the TTS significantly.

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