  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.
