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

TL;DR

This paper demonstrates how quantum annealers can be used to perform regression and compression on lattice QCD data, achieving efficient predictions and lossy compression with small error margins.

Contribution

It introduces novel machine learning algorithms for lattice QCD data that leverage quantum annealers for solving complex binary optimization problems.

Findings

Quantum annealers effectively solve regression and compression tasks for lattice QCD data.

The compression algorithm achieves low reconstruction error with few binary coefficients.

Regression algorithms predict lattice observables accurately on unseen configurations.

Abstract

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.