Optimizing qubit Hamiltonian parameter estimation algorithms using PSO

TL;DR

This paper introduces a Bayesian-based method for estimating qubit Hamiltonian parameters, optimizing a non-adaptive algorithm with particle swarm optimization and comparing it to existing schemes.

Contribution

It presents a novel application of PSO to optimize non-adaptive quantum parameter estimation algorithms for qubits.

Findings

PSO-optimized algorithm outperforms previous locally-optimal schemes

The method is effective under slow measurement conditions

Provides a framework for non-adaptive quantum parameter estimation

Abstract

We develop qubit Hamiltonian single parameter estimation techniques using a Bayesian approach. The algorithms considered are restricted to projective measurements in a fixed basis, and are derived under the assumption that the qubit measurement is much slower than the characteristic qubit evolution. We optimize a non-adaptive algorithm using particle swarm optimization (PSO) and compare with a previously-developed locally-optimal scheme.