W
William G. Macready
Researcher at D-Wave Systems
Publications - 93
Citations - 15788
William G. Macready is an academic researcher from D-Wave Systems. The author has contributed to research in topics: Quantum computer & Optimization problem. The author has an hindex of 34, co-authored 91 publications receiving 13024 citations. Previous affiliations of William G. Macready include IBM & Santa Fe Institute.
Papers
More filters
Patent
Quantum processor-based systems, methods and apparatus for solving problems as logic circuits
TL;DR: In this paper, the factoring problem is expressed as a discrete optimization problem, which is solved using a quantum processor, and the output of the quantum processor is clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that correspond to clamped output(s).
Patent
Systems, devices, and methods for solving computational problems
TL;DR: In this paper, a digital processor is configured to track computational problem processing requests received from a plurality of different users, and to track at least one of a status and a processing cost for each of the computations.
Posted Content
Construction of Energy Functions for Lattice Heteropolymer Models: Efficient Encodings for Constraint Satisfaction Programming and Quantum Annealing
Ryan Babbush,Alejandro Perdomo-Ortiz,Alejandro Perdomo-Ortiz,Bryan O'Gorman,William G. Macready,Alán Aspuru-Guzik +5 more
TL;DR: In this paper, a case study for encoding related combinatorial optimization problems in a form suitable for adiabatic quantum optimization is presented, where the authors demonstrate how to constrain and embed lattice heteropolymer problems using several strategies, each striking a unique balance between number of constraints, complexity of constraints and number of variables.
Book ChapterDOI
Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization
TL;DR: This review explains how to recast combinatorial optimization problems as constraint satisfaction problems such as linear programming, maximum satisfiability, and pseudo-boolean optimization, and shows how to constrain and embed lattice heteropolymer problems using several strategies.
Patent
Systems and methods for finding quantum binary optimization problems
TL;DR: In this article, the Ising model penalty function is used to find quantum binary optimization problems and associated gap values employing a variety of techniques, and the penalty gap is defined as the gap separating a set of feasible solutions to the constraint from a set that are infeasible.