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We present a new lossy compression algorithm for statistical floating-pointdata through a representation learning with binary variables. The algorithmfinds a set of basis vectors and their binary coefficients that preciselyreconstruct the original data. The optimization for the basis vectors isperformed classically, while binary coefficients are retrieved through bothsimulated and quantum annealing for comparison. A bias correction procedure isalso presented to estimate and eliminate the error and bias introduced from theinexact reconstruction of the lossy compression for statistical data analyses.The compression algorithm is demonstrated on two different datasets of latticequantum chromodynamics simulations. The results obtained using simulatedannealing show 3.5 times better compression performance than the algorithmsbased on a neural-network autoencoder and principal component analysis.Calculations using quantum annealing also show promising results, butperformance is limited by the integrated control error of the quantumprocessing unit, which yields large uncertainties in the biases and couplingparameters. Hardware comparison is further studied between the previousgeneration D-Wave 2000Q and the current D-Wave Advantage system. Our studyshows that the Advantage system is more likely to obtain low-energy solutionsfor the problems than the 2000Q.

Lossy compression of statistical data using quantum annealer