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
Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling
Nicholas H. Angello,Vandana Rathore,Wiktor Beker,Agnieszka Wolos,E. Jíra,Rafał Roszak,Tony C. Wu,Charles M. Schroeder,Alán Aspuru-Guzik,Bartosz A. Grzybowski,Martin D Burke +10 more
TL;DR: A simple closed-loop workflow that leverages data-guided matrix down-selection, uncertainty-minimizing machine learning, and robotic experimentation to discover general reaction conditions that double the average yield relative to a widely used benchmark that was previously developed using traditional approaches is reported.
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Exploiting locality in quantum computation for quantum chemistry
TL;DR: In this article, the authors bring together known results about the locality of physical interactions from quantum chemistry with ideas from quantum computation and show that the utilization of spatial locality combined with the Bravyi-Kitaev transformation offers an improvement in the scaling of known quantum algorithms for quantum chemistry.
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ChemOS: An orchestration software to democratize autonomous discovery.
Loïc M. Roch,Florian Häse,Christoph Kreisbeck,Teresa Tamayo-Mendoza,Lars P. E. Yunker,Jason E. Hein,Alán Aspuru-Guzik +6 more
TL;DR: This paper proposes and develops an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories, and it enables remote control of automated laboratories.
123
Use machine learning to find energy materials.
TL;DR: Artificial intelligence can speed up research into new photovoltaic, battery and carbon-capture materials, argue Edward Sargent, Alán Aspuru-Guzikand colleagues.
Bayesian network structure learning using quantum annealing
TL;DR: A method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm is introduced by introducing an efficient reformulation of a standard posterior-probability scoring function on graphs as a pseudo-Boolean function, which is equivalent to a system of 2-body Ising spins.