A Quantum-Quantum Metropolis Algorithm

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A Quantum-Quantum Metropolis Algorithm

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Title: A Quantum-Quantum Metropolis Algorithm
Author: Yung, Man-Hong; Aspuru-Guzik, Alan

Note: Order does not necessarily reflect citation order of authors.

Citation: Yung, Man-Hong and Alan Aspuru-Guzik. 2012. A quantum-quantum metropolis algorithm. Proceedings of the National Academy of Sciences 109(3): 754-759.
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Abstract: The classical Metropolis sampling method is a cornerstone of many statistical modeling applications that range from physics, chemistry, and biology to economics. This method is particularly suitable for sampling the thermal distributions of classical systems. The challenge of extending this method to the simulation of arbitrary quantum systems is that, in general, eigenstates of quantum Hamiltonians cannot be obtained efficiently with a classical computer. However, this challenge can be overcome by quantum computers. Here, we present a quantum algorithm which fully generalizes the classical Metropolis algorithm to the quantum domain. The meaning of quantum generalization is twofold: The proposed algorithm is not only applicable to both classical and quantum systems, but also offers a quantum speedup relative to the classical counterpart. Furthermore, unlike the classical method of quantum Monte Carlo, this quantum algorithm does not suffer from the negative-sign problem associated with fermionic systems. Applications of this algorithm include the study of low-temperature properties of quantum systems, such as the Hubbard model, and preparing the thermal states of sizable molecules to simulate, for example, chemical reactions at an arbitrary temperature.
Published Version: doi:10.1073/pnas.1111758109
Other Sources: http://arxiv.org/abs/1011.1468
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:8366164

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  • FAS Scholarly Articles [7103]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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