机器学习辅助绝热量子算法设计

Machine learning assisted quantum adiabatic algorithm design

量子计算在近十年取得了长足的进展.随着量子调控技术达到前所未有的高度,包括超导量子比特、光量子器件、原子系综等在内的量子实验平台都进入到了崭新的时代.目前在特定计算任务上超越经典的量子计算优势也已经被报道.其中一种可以有效运用可控量子器件的计算方案是采用绝热量子计算.绝热量子计算中算法的选择与研究至关重要,其将直接决定量子计算优势是否能够最大限度地被挖掘.本综述主要介绍近期机器学习在绝热量子算法设计方面的应用,并讲述该计算架构在3-SAT和Grover搜索等问题上的应用.通过与未经机器学习优化设计的绝热量子算法对比,研究表明机器学习方法的应用可以极大提高绝热量子算法的计算效率.

Quantum computing has made dramatic progress in the last decade.The quantum platforms including superconducting qubits,photonic devices,and atomic ensembles,have all reached a new era,with unprecedented quantum control capability developed.Quantum computation advantage over classical computers has been reported on certain computation tasks.A promising computing protocol of using the computation power in these controllable quantum devices is implemented through quantum adiabatic computing,where quantum algorithm design plays an essential role in fully using the quantum advantage.Here in this paper,we review recent developments in using machine learning approach to design the quantum adiabatic algorithm.Its applications to 3-SAT problems,and also the Grover search problems are discussed.