QFitter -- A Quantum Fitting Framework Applied to Effective Field Theories

TL;DR

QFitter leverages quantum annealing to efficiently perform global fits of Effective Field Theory parameters, overcoming classical optimization limitations and handling complex, non-convex $oldsymbol{ extit{ ext{chi}}}^2$ functions with multiple coefficients and observables.

Contribution

This work introduces QFitter, a quantum annealing framework that improves EFT parameter fitting by reliably finding global minima in non-convex optimization landscapes.

Findings

QFitter successfully fits at least eight coefficients including quadratic terms.

It can incorporate unlimited observables without increasing qubit requirements.

QFitter outperforms classical methods in locating the global minimum for non-convex $oldsymbol{ extit{ ext{chi}}}^2$ functions.

Abstract

The use of experimental data to constrain the values of the Wilson coefficients of an Effective Field Theory (EFT) involves minimising a $\chi^2$ function that may contain local minima. Classical optimisation algorithms can become trapped in these minima, preventing the determination of the global minimum. The quantum annealing framework has the potential to overcome this limitation and reliably find the global minimum of non-convex functions. We present QFitter, a quantum annealing method to perform EFT fits. Using a state-of-the-art quantum annealer, we show with concrete examples that QFitter can be used to fit sets of at least eight coefficients, including their quadratic contributions. An arbitrary number of observables can be included without changing the required number of qubits. We provide an example in which $\chi^2$ is non-convex and show that QFitter can find the global minimum more accurately than its classical alternatives.