Quantum Computing and Phase Transitions in Combinatorial Search
Tad Hogg
备注：
29 pages, uuencoded tar compressed postscript in a self-unpacking shell script created using the uufiles script. For related information on phase transitions in search see ftp://parcftp.xerox.com/pub/dynamics/constraints.html
发表信息：
J. of Artificial Intelligence Research 4,91-128 (1996)
We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by exploiting the same aspects of problem structure as used by classical backtrack methods to avoid unproductive search choices. This quantum algorithm is much more likely to find solutions than the simple direct use of quantum parallelism. Furthermore, empirical evaluation on small problems shows this quantum algorithm displays the same phase transition behavior, and at the same location, as seen in many previously studied classical search methods. Specifically, difficult problem instances are concentrated near the abrupt change from underconstrained to overconstrained problems.
Quantum-enhanced Markov Chain Monte Carlo for Combinatorial Optimization
IBM Quantum, IBM Research Europe – Hursley, United Kingdom（IBM量子，IBM欧洲研究-赫斯利）；IBM Quantum, IBM Research Europe – Zurich, Switzerland（IBM量子，IBM欧洲研究-苏黎世）；The Hartree Centre, STFC, Sci-Tech Daresbury, United Kingdom（哈特里中心，STFC，达尔斯伯里科技）；The Open Quantum Institute, Geneva, Switzerland（开放量子研究所）；IBM Quantum, IBM Research Europe – Böblingen, Germany（IBM量子，IBM欧洲研究-博布林根）
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Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) model which is presently the most widely applied optimization model in the quantum computing area, an…
The intractability of deterministic solutions in solving $\mathcal{NP}$-Hard Combinatorial Optimisation Problems (COP) is well reported in the literature. One mechanism for overcoming this difficulty has been the use of efficient COP non-deterministic approaches. However, with the advent of quantum technology, these modern devices' potential to overcome this tractability limitation requires exploration. This paper juxtaposes classical and quantum optimisation algorithms' performance to solve two common COP, the Travelling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP). Two a…
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
National Institute of Advanced Industrial Science and Technology（日本先进工业科学技术研究所）；NVIDIA Corporation（英伟达公司）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；DENSO CORPORATION（电装公司）
