Fermi-Dirac thermal measurements: A framework for quantum hypothesis testing and semidefinite optimization

TL;DR

This paper introduces Fermi-Dirac thermal measurements as an innovative approach for quantum hypothesis testing and semidefinite optimization, leveraging fermionic interpretations and thermal measurement techniques to improve quantum algorithms.

Contribution

It presents a novel framework connecting quantum measurements with Fermi-Dirac distributions, leading to new quantum machine learning models and optimization paradigms.

Findings

Fermi-Dirac measurements approximate optimal hypothesis testing performance at low temperatures.

Parameters of Fermi-Dirac measurements can be learned via classical or hybrid algorithms.

The approach enables solving semidefinite programs through thermal measurements on quantum computers.

Abstract

Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis testing, wherein the goal is to perform an optimal measurement for arriving at a correct conclusion. Mathematically, a measurement operator is Hermitian with eigenvalues in [0,1]. By noticing that this constraint on each eigenvalue is the same as that imposed on fermions by the Pauli exclusion principle, we interpret every eigenmode of a measurement operator as an independent effective fermionic mode. Under this perspective, various objective functions in quantum hypothesis testing can be viewed as the total expected energy associated with these fermionic occupation numbers. By instead fixing a temperature and minimizing the total expected fermionic free energy, we find that optimal measurements for these modified objective functions are Fermi-Dirac thermal measurements, wherein their eigenvalues are specified by Fermi-Dirac distributions. In the low-temperature limit, their performance closely approximates that of optimal measurements for quantum hypothesis testing, and we show that their parameters can be learned by classical or hybrid quantum-classical optimization algorithms. This leads to a new quantum machine-learning model, termed Fermi-Dirac machines, consisting of parameterized Fermi-Dirac thermal measurements-an alternative to quantum Boltzmann machines based on thermal states. Beyond hypothesis testing, we show how general semidefinite optimization problems can be solved using this approach, leading to a novel paradigm for semidefinite optimization on quantum computers, in which the goal is to implement thermal measurements rather than prepare thermal states. Finally, we propose quantum algorithms for implementing Fermi-Dirac thermal measurements, and we also propose second-order hybrid quantum-classical optimization algorithms.