Quantum algorithms for quantum chemistry and quantum materials science

• URL: http://arxiv.org/abs/2001.03685v2
• Date: Sat, 11 Jul 2020 02:04:20 GMT
• Title: Quantum algorithms for quantum chemistry and quantum materials science
• Authors: Bela Bauer and Sergey Bravyi and Mario Motta and Garnet Kin-Lic Chan
• Abstract summary: We briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer. We take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal state simulation, and analyze their strengths and weaknesses for future developments.
• Score: 2.867517731896504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynman's idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics, that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal state simulation, and analyze their strengths and weaknesses for future developments.

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