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
Quantum computing at the frontiers of biological sciences.
Prashant Emani,Jonathan Warrell,Alan Anticevic,Stefan Bekiranov,Michael J. Gandal,Michael J. McConnell,Guillermo Sapiro,Alán Aspuru-Guzik,Justin T. Baker,Justin T. Baker,Matteo Bastiani,Matteo Bastiani,John D. Murray,Stamatios N. Sotiropoulos,Stamatios N. Sotiropoulos,Jacob M. Taylor,Jacob M. Taylor,Geetha Senthil,Thomas Lehner,Mark Gerstein,Aram W. Harrow +20 more
TL;DR: In this paper, the potential for quantum computing to aid in the merging of insights across different areas of biological sciences is discussed, and the potential of quantum computing in the biomedical domain is discussed.
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Preparation of many-body states for quantum simulation.
TL;DR: The present algorithm is able to prepare general pure and mixed many-particle states of any number of particles and operates in time that is polynomial in all the essential descriptors of the system, the number ofarticles, the resolution of the lattice, and the inverse of the maximum final error.
Automatic differentiation in quantum chemistry with an application to fully variational Hartree-Fock
TL;DR: In this article, the authors demonstrate that automatic differentiation can be used to compute gradients with respect to any parameter throughout a complete quantum chemistry method, and leverage the obtained gradients to optimize the parameters of one-particle basis sets in the context of the floating Gaussian framework.
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Machine learning exciton dynamics
Florian Häse,Stéphanie Valleau,Edward Pyzer-Knapp,Alán Aspuru-Guzik +3 more
Abstract: Machine learning ground state QM/MM for accelerated computation of exciton dynamics.
Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning
Gabriel dos Passos Gomes,Robert Pollice,Alán Aspuru-Guzik +2 more
- 01 Feb 2021
TL;DR: The vision for the future of homogeneous catalyst design and the role of ML in navigating this maze is outlined and a whole new way to approach data-intensive problems is revolutionized.