A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

Nguyen, Nga T.; Kenyon, Garrett T.; Yoon, Boram

doi:10.1038/s41598-020-67769-x

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer

Journal Article · Thu Jul 02 04:00:00 EDT 2020 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-020-67769-x· OSTI ID:1648089

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Consortium, Los Alamos, NM (United States)

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: LDRD; USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: 89233218CNA000001; AC05-00OR22725

OSTI ID:: 1648089

Report Number(s):: LA-UR--19-31717

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 10; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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