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
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Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation
TL;DR: In this paper, a classically solvable model that leads to optimized low-depth quantum circuits leveraging separable pair approximations is presented. But the model is not suitable as a baseline circuit for emerging quantum hardware and can, in the long term, provide significantly improved initial states for quantum algorithms.
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The Kitaev?Feynman clock for open quantum systems
TL;DR: In this paper, the ground states of an ensemble of non-Hermitian Kitaev-Feynman clock Hamiltonians yield stochastic trajectories, which unravel the Lindblad master equation.
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Can Mixed-Metal Surfaces Provide an Additional Enhancement to SERS?
Roberto Olivares-Amaya,Dmitrij Rappoport,Philip Munoz,Paul Peng,Eric Mazur,Alán Aspuru-Guzik +5 more
TL;DR: In this article, the chemical contribution to surface-enhanced Raman scattering (SERS) in mixed-metal substrates, both experimentally and by computer simulation, was explored.
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A molecular computing approach to solving optimization problems via programmable microdroplet arrays
Si Yue Guo,Pascal Friederich,Pascal Friederich,Yudong Cao,Tony C. Wu,Chris Forman,Douglas Mendoza,Douglas Mendoza,Matthias Degroote,Andrew C. Cavell,Veronica K. Krasecki,Riley J. Hickman,Abhishek Sharma,Leroy Cronin,Nathan C. Gianneschi,Randall H. Goldsmith,Alán Aspuru-Guzik +16 more
TL;DR: This work presents a microdroplet array molecular computer to solve combinatorial optimization problems by employing an Ising Hamiltonian to map problems heuristically to droplet-droplet interactions.
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Predicting 3D shapes, masks, and properties of materials inside transparent containers, using the TransProteus CGI dataset
TL;DR: TransProteus, a dataset, and methods for predicting the 3D structure and properties of materials inside transparent vessels from a single image are presented.
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