Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
This work addresses data handling challenges in lattice QCD simulations for physicists, but it is incremental as it applies existing quantum annealing methods to a specific domain.
The paper tackled the problem of predicting and compressing lattice QCD data by using quantum annealers to solve NP-hard binary optimization problems in machine learning algorithms, achieving reconstruction errors much smaller than statistical fluctuations with a small number of binary coefficients.
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.