Quantum Computer as a Probabilistic Inference Engine
Robert R. Tucci
备注：
V1: 33 pages (files: 1 .tex, 3 .sty, 16 .eps) V2: 49 pages (files: 1 .tex, 2 .sty, 19 .eps) V2 is a substantial revision, 4 years after V1. Kept about 50% of V1. Fixed/Improved main technique proposed, and explained how to combine it with Grover's algorithm
We propose a new class of quantum computing algorithms which generalize many standard ones. The goal of our algorithms is to estimate probability distributions. Such estimates are useful in, for example, applications of Decision Theory and Artificial Intelligence, where inferences are made based on uncertain knowledge. The class of algorithms that we propose is based on a construction method that generalizes a Fredkin-Toffoli (F-T) construction method used in the field of classical reversible computing. F-T showed how, given any binary deterministic circuit, one can construct another binary deterministic circuit which does the same calculations in a reversible manner. We show how, given any classical stochastic network (classical Bayesian net), one can construct a quantum network (quantum Bayesian net). By running this quantum Bayesian net on a quantum computer, one can calculate any conditional probability that one would be interested in calculating for the original classical Bayesian net. Thus, we generalize the F-T construction method so that it can be applied to any classical stochastic circuit, not just binary deterministic ones. We also show that, in certain situations, our class of algorithms can be combined with Grover's algorithm to great advantage.
Probabilistic modeling over permutations using quantum computers
Xanadu Quantum Technologies（Xanadu量子技术公司）；Department of Physics, University of Trieste（特里斯特大学物理系）；European Organization for Nuclear Research (CERN)（欧洲核子研究中心）；Department of Physics and Earth Science, University of Ferrara（费拉拉大学物理与地球科学系）
Department of Electrical and Computer Engineering, University of California, Santa Barbara（加州大学圣芭芭拉分校电气与计算机工程系）；Radboud University, Institute for Molecules and Materials（拉德堡德大学分子与材料研究所）；University of California, Santa Barbara（加州大学圣芭芭拉分校）
Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；University of Messina（墨西拿大学）；Politecnico di Bari（巴里理工大学）；Istituto Nazionale di Geofisica e Vulcanologia（意大利国家地球物理学和火山学研究所）；Carnegie Mellon University（卡内基梅隆大学）；USRA Research Institute for Advanced Computer Science (RIACS)（美国大学空间研究协会高级计算机科学研究所）；Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center（NASA艾姆斯研究中心量子人工智能实验室）；Radboud University（拉德堡德大学）；Jagiellonian University（雅盖隆大学）；Hewlett Packard Labs（惠普实验室）
Random matrix perspective on probabilistic error cancellation
Institute of Physics, University of Bonn（波恩大学物理研究所）；CeFEMA-LaPMET, Departamento de Fisica, Instituto Superior Tecnico, Universidade de Lisboa（里斯本大学高等理工学院物理系）；Faculty of Mathematics and Physics, University of Ljubljana（卢布尔雅那大学数学与物理学院）；Department of Computer Science, OsloMet – Oslo Metropolitan University（奥斯陆城市大学计算机科学系）；Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University（亚盖隆大学物理、天文学与应用计算机科学学院）；Center for Theoretical Physics, Polish Academy of Sciences（波兰科学院理论物理中心）
