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
Faster than Classical Quantum Algorithm for dense Formulas of Exact Satisfiability and Occupation Problems
TL;DR: The proposed quantum algorithm for solving the Exact Satisfiability (XSAT) problem is faster than the classical WalkSAT and Adiabatic Quantum Optimization for random instances with a density of constraints close to the satisfiability threshold, the regime in which instances are typically the hardest to solve.
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Neural message passing on high order paths
Daniel Flam-Shepherd,Tony C. Wu,Pascal Friederich,Alán Aspuru-Guzik +3 more
- 01 Dec 2021
TL;DR: This work generalizes graph neural nets to pass messages and aggregate across higher order paths, which allows for information to propagate over various levels and substructures of the graph.
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•Posted Content
Neural Message Passing on High Order Paths
TL;DR: In this article, the authors generalize graph neural networks to pass messages and aggregate across higher-order paths, allowing information to propagate over various levels and substructures of the graph.
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Feynman's clock, a new variational principle, and parallel-in-time quantum dynamics.
TL;DR: A construction inspired by quantum computation that allows one to use virtually any model for a ground-state wavefunction to model quantum many-body dynamics and formulate it in a way that naturally leads to a parallel-in-time algorithm.
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
Cynthia Shen,Mario Krenn,Sagi Eppel,Alán Aspuru-Guzik +3 more
- 09 Jun 2021
TL;DR: PASITHEA as discussed by the authors exploits the use of gradients by directly reversing the learning process of a neural network, which is trained to predict real-valued chemical properties, which forms an inverse regression model, which can be capable of generating molecular variants optimized for a certain property.
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