Unfolding as Quantum Annealing

TL;DR

This paper demonstrates how quantum annealing can be applied to solve likelihood-based unfolding problems in high-energy physics, showing promising results and potential for handling systematic uncertainties.

Contribution

It introduces a novel approach to recast unfolding as a QUBO problem suitable for quantum annealers, extending quantum computing applications in physics data analysis.

Findings

Unfolded distributions closely match original data.

Quantum annealing effectively solves the inverse problem.

Method can incorporate systematic uncertainties.

Abstract

High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations. We present an implementation of likelihood-based regularized unfolding on a quantum computer. The inverse problem is recast in terms of quadratic unconstrained binary optimization (QUBO), which has the same form of the Ising hamiltonian and hence it is solvable on a programmable quantum annealer. We tested the method using a model that captures the essence of the problem, and compared the results with a baseline method commonly used in precision measurements at the Large Hadron Collider (LHC) at CERN. The unfolded distribution is in very good agreement with the original one. We also show how the method can be extended to include the effect of nuisance parameters representing sources of systematic uncertainties affecting the measurement.