Alán Aspuru-Guzik
University of Toronto
664 Papers
4.7K Citations
Alán Aspuru-Guzik is an academic researcher from University of Toronto. The author has contributed to research in topics: Quantum computer & Quantum. The author has an hindex of 97, co-authored 628 publications. Previous affiliations of Alán Aspuru-Guzik include D-Wave Systems & National Autonomous University of Mexico.
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Papers
Accelerating the computation of bath spectral densities with super-resolution
Thomas Markovich,Samuel M. Blau,John Parkhill,Christoph Kreisbeck,Jacob N. Sanders,Xavier Andrade,Alán Aspuru-Guzik +6 more
TL;DR: This paper applies a novel signal processing technique, known as super-resolution, combined with a dictionary of physically motivated bath modes to derive spectral densities from molecular dynamics simulations, which reduces the required simulation time and provides a more accurate spectral density than can be obtained via standard Fourier transform methods.
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Partitioning Quantum Chemistry Simulations with Clifford Circuits
Philipp Schleich,Joseph Boen,Lukasz Cincio,Abhinav Anand,Jakob S. Kottmann,Sergei Tretiak,Pavel A. Dub,Alán Aspuru-Guzik +7 more
- 02 Mar 2023
TL;DR: In this article , the authors investigate the limits of classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver and demonstrate their approach on a set of molecules of interest and investigate the extent of their methodology's reach.
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Bayesian Variational Optimization for Combinatorial Spaces.
TL;DR: A variational Bayesian Optimization method that combines variational optimization and continuous relaxations to the optimization of the acquisition function for Bayesian optimization and has the capability of optimizing problems with large data size and data dimensions is introduced.
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Assigning Confidence to Molecular Property Prediction.
AkshatKumar Nigam,Robert Pollice,Matthew F. D. Hurley,Riley J. Hickman,Matteo Aldeghi,Naruki Yoshikawa,Seyone Chithrananda,Vincent A. Voelz,Alán Aspuru-Guzik +8 more
TL;DR: In this article, the authors assess uncertainty in property prediction models and investigate how these uncertainties propagate to generative models, as they are usually coupled with property predictors, which leads to better-informed experimental validations, more reliable predictions and to more realistic expectations of the entire workflow.
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Generator evaluator-selector net: a modular approach for panoptic segmentation.
Sagi Eppel,Alán Aspuru-Guzik +1 more
- 24 Aug 2019
TL;DR: The result is a trial and error evolutionary approach in which a generator that guesses segments with low average accuracy, but with wide variability, can still produce good results when coupled with an accurate evaluator.
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