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We present regression and compression algorithms for lattice QCD datautilizing the efficient binary optimization ability of quantum annealers. Inthe regression algorithm, we encode the correlation between the input andoutput variables into a sparse coding machine learning algorithm. The trainedcorrelation pattern is used to predict lattice QCD observables of unseenlattice configurations from other observables measured on the lattice. In thecompression algorithm, we define a mapping from lattice QCD data offloating-point numbers to the binary coefficients that closely reconstruct theinput data from a set of basis vectors. Since the reconstruction is not exact,the mapping defines a lossy compression, but, a reasonably small number ofbinary coefficients are able to reconstruct the input vector of lattice QCDdata with the reconstruction error much smaller than the statisticalfluctuation. In both applications, we use D-Wave quantum annealers to solve theNP-hard binary optimization problems of the machine learning algorithms.

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