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Abstract
Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.
The extent of problems in quantum chemistry for which quantum algorithms could provide a speedup is still unclear, as well as the kind of speedup one should expect. Here, the authors look at the problem of ground state energy estimation, and gather theoretical and numerical evidence for the fact that an exponential quantum advantage is unlikely for generic problems of interest.
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1 California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890)
2 Columbia University, Department of Chemistry, New York, USA (GRID:grid.21729.3f) (ISNI:0000000419368729)
3 University of California, Department of Mathematics, Berkeley, USA (GRID:grid.47840.3f) (ISNI:0000 0001 2181 7878)
4 AWS Center for Quantum Computing, Pasadena, USA (GRID:grid.467171.2) (ISNI:0000 0001 0316 7795)
5 Virginia Tech, Department of Chemistry, Blacksburg, USA (GRID:grid.438526.e) (ISNI:0000 0001 0694 4940); Los Alamos National Laboratory, Theoretical Division, Los Alamos, USA (GRID:grid.148313.c) (ISNI:0000 0004 0428 3079)
6 AWS Center for Quantum Computing, Pasadena, USA (GRID:grid.467171.2) (ISNI:0000 0001 0316 7795); Amazon Quantum Solutions Lab, Seattle, USA (GRID:grid.467171.2)
7 Google Quantum AI, Venice, USA (GRID:grid.420451.6) (ISNI:0000 0004 0635 6729)
8 AWS Center for Quantum Computing, Pasadena, USA (GRID:grid.467171.2) (ISNI:0000 0001 0316 7795); California Institute of Technology, Institute for Quantum Information and Matter, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890)
9 Riverlane, Cambridge, UK (GRID:grid.510713.1)
10 Virginia Tech, Department of Chemistry, Blacksburg, USA (GRID:grid.438526.e) (ISNI:0000 0001 0694 4940)
11 University of California, Department of Mathematics, Berkeley, USA (GRID:grid.47840.3f) (ISNI:0000 0001 2181 7878); Lawrence Berkeley National Laboratory, Computational Research Division, Berkeley, USA (GRID:grid.184769.5) (ISNI:0000 0001 2231 4551)