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 Computer-Aided design of Quantum Optics Hardware
Jakob S. Kottmann,Mario Krenn,Thi Ha Kyaw,Sumner Alperin-Lea,Alán Aspuru-Guzik +4 more
- 28 Apr 2021
TL;DR: This work presents the concept of quantum computer designed quantum hardware and applies it to the field of quantum optics and shows explicitly how digital quantum simulation of Boson sampling experiments can be realized.
On thermodynamic inconsistencies in several photosynthetic and solar cell models and how to fix them
David Gelbwaser-Klimovsky,Alán Aspuru-Guzik +1 more
TL;DR: The standard theoretical models for solar cells and photosynthetic systems exhibit thermodynamic inconsistencies. The analysis reveals the origin of these inconsistencies and proposes solutions to fix them.
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Practical Witness for Electronic Coherences
TL;DR: In this paper, the authors demonstrate a method for practically implementing such a test, whereby pump-probe signals are taken at several different pulse durations and used to extrapolate to the ultrashort-pulse limit.
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Exponentially more precise quantum simulation of fermions II: Quantum chemistry in the CI matrix representation
TL;DR: A quantum algorithm for the simulation of molecular systems that is asymptotically more efficient than all previous algorithms in the literature in terms of the main problem parameters is presented.
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Experimental demonstration of a quantum generative adversarial network for continuous distributions
TL;DR: A hybrid architecture for quantum generative adversarial networks (QGANs) is employed and their robustness in the presence of noise is studied, paving the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate‐scale quantum devices.
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