Lossy compression of statistical data using quantum annealer

Yoon, Boram; Nguyen, Nga T. T.; Chang, Chia Cheng; Rrapaj, Ermal

doi:10.1038/s41598-022-07539-z

Lossy compression of statistical data using quantum annealer

Journal Article · Wed Mar 09 00:00:00 EST 2022 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-022-07539-z· OSTI ID:1854018

Yoon, Boram; Nguyen, Nga T. T.; Chang, Chia Cheng; Rrapaj, Ermal

Abstract

We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3–3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.

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

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

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1854018

Alternate ID(s):: OSTI ID: 1868287

Report Number(s):: LA-UR-21-25525; 3814; PII: 7539

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 12; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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