  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.
