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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
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Experimental determination of Ramsey numbers.
TL;DR: This computation is the largest experimental implementation of a scientifically meaningful adiabatic evolution algorithm that has been done to date and correctly determines the Ramsey numbers R(3,3) and R(m,2) for 4≤m≤8.
Posted Content
Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization
TL;DR: This work describes how to formulate image recognition, which is a canonical NP-hard AI problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which corresponds to the input format required for D-Wave superconducting adiabatic quantum computing (AQC) processors.
Patent
Systems, devices and methods for interconnected processor topology
TL;DR: In this paper, a quantum processor may include a plurality of elongated qubits that are disposed with respect to one another such that each qubit may selectively be directly coupled to each of the other qubits via a single coupling device.
Patent
Adaptive and reliable system and method for operations management
Isaac Saias,Vince Darley,Stuart A. Kauffman,Fred Federspiel,Judith Cohn,Bennett Levitan,Robert Macdonald,William G. Macready,Carl Tollander +8 more
TL;DR: In this article, the authors present a comprehensive system and method for operations management which has the reliability and adaptability to handle failures and changes respectively within the economic environment, using technology graphs, landscape representations and automated markets.
Posted Content
Training a Binary Classifier with the Quantum Adiabatic Algorithm
TL;DR: This paper describes how to make the problem of binary classification amenable to quantum computing, and finds that the resulting classifier outperforms a widely used state-of-the-art method, AdaBoost, on a variety of benchmark problems.