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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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Improved Gradient-Based Optimization Over Discrete Distributions.
TL;DR: It is demonstrated empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks and derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias.
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Weakly Supervised Semantic Image Segmentation with Self-correcting Networks
TL;DR: A principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels, called the weak set.
Patent
Systems and methods for solving combinatorial problems
TL;DR: In this article, the Travelling Salesman Problem is discussed as an example of a combinatorial problem that may be solved using these systems and methods, and a quantum processor may be used to generate permutations by the permutation network by mapping the state of each switch in the network to the states of a respective qubit in the quantum processor.
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Investigating the Performance of an Adiabatic Quantum Optimization Processor
Kamran Karimi,Neil G. Dickson,Firas Hamze,Mohammad H. Amin,Marshall Drew-Brook,Fabián A. Chudak,Paul I. Bunyk,William G. Macready,Geordie Rose +8 more
TL;DR: In this paper, the median adiabatic times determined by the minimum gap during the quantum optimization for an NP-hard Ising spin glass instance class with up to 128 binary variables were calculated.
Patent
Systems and methods for analog processing of problem graphs having arbitrary size and/or connectivity
Murray C. Thom,Aidan Roy,Fabián A. Chudak,Zhengbing Bian,William G. Macready,Robert B. Israel,Kelly T. R. Boothby,Sheir Yarkoni,Yanbo Xue,Dmytro Korenkevych +9 more
TL;DR: In this paper, a quantum processor is used to determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions.