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
Resource Efficient Gadgets for Compiling Adiabatic Quantum Optimization Problems
TL;DR: In this paper, the ground state of an arbitrary k-local, optimization Hamiltonian can be encoded as the ground-state of a (k-1)-local optimization Hamiltonians using the least possible number of ancilla qubits.
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Environment-Assisted Quantum Walks in Photosynthetic Energy Transfer
TL;DR: In this paper, the role of quantum interference effects in energy transfer dynamics of molecular arrays interacting with a thermal bath within the Lindblad formalism is investigated, and different physical effects of coherence and decoherence processes are explored via a universal measure for the energy transfer efficiency and its susceptibility.
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•Book
Ultrafast Spectroscopy: Quantum information and wavepackets
Joel Yuen-Zhou,Jacob J. Krich,Ivan Kassal,Allan S. Johnson,Alán Aspuru-Guzik +4 more
- 05 Sep 2014
TL;DR: The core of the book is the section on pump-probe spectroscopy as on understanding its mathematical description, more complex and multidimensional spectroscopies become easily understood derivatives and readers will be fully equipped with the tools to devise and undertake well-reasoned spectroscopic experiments.
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MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.
TL;DR: The MultiDK method improves both the speed and accuracy of molecular property prediction and applies the method to the discovery of electrolyte molecules for aqueous redox flow batteries to obtain more relevant features for machine learning.
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Golem: An algorithm for robust experiment and process optimization
TL;DR: Golem as discussed by the authors is an algorithm that is agnostic to the choice of experiment planning strategy and that enables robust experiment and process optimization by identifying optimal solutions that are robust to input uncertainty, thus ensuring the reproducible performance of optimized experimental protocols and processes.