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Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer

Journal Article · · PoS - Proceedings of Science
DOI:https://doi.org/10.22323/1.396.0143· OSTI ID:1887146
 [1];  [2];  [1];  [1];  [3]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Institute of Physical and Chemical Research (RIKEN), Wako (Japan); University of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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We present regression and compression algorithms for lattice QCD data utilizing the efficient binary optimization ability of quantum annealers. In the regression algorithm, we encode the correlation between the input and output variables into a sparse coding machine learning algorithm. The trained correlation pattern is used to predict lattice QCD observables of unseen lattice configurations from other observables measured on the lattice. In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors. Since the reconstruction is not exact, the mapping defines a lossy compression, but, a reasonably small number of binary coefficients are able to reconstruct the input vector of lattice QCD data with the reconstruction error much smaller than the statistical fluctuation. In both applications, we use D-Wave quantum annealers to solve the NP-hard binary optimization problems of the machine learning algorithms.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF); Heising-Simons Foundation
Grant/Contract Number:
89233218CNA000001; AC02-05CH11231
OSTI ID:
1887146
Report Number(s):
LA-UR-21-31665
Journal Information:
PoS - Proceedings of Science, Journal Name: PoS - Proceedings of Science Vol. 396; ISSN 1824-8039
Publisher:
SISSACopyright Statement
Country of Publication:
United States
Language:
English

References (1)

Generalization capabilities of translationally equivariant neural networks dataset January 2021

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79 ASTRONOMY AND ASTROPHYSICS