Quantum System Identification: Hamiltonian Estimation using Spectral and Bayesian Analysis

TL;DR

This paper explores methods for identifying quantum system Hamiltonians from experimental data, focusing on spectral and Bayesian analysis techniques to improve parameter estimation under resource constraints.

Contribution

It introduces a Bayesian estimation procedure tailored for quantum Hamiltonian identification, considering prior structural information and resource limitations.

Findings

Bayesian method improves parameter estimation accuracy.

Limits on Hamiltonian identifiability are characterized.

Method demonstrates effectiveness on a model quantum system.

Abstract

Identifying the Hamiltonian of a quantum system from experimental data is considered. General limits on the identifiability of model parameters with limited experimental resources are investigated, and a specific Bayesian estimation procedure is proposed and evaluated for a model system where a-priori information about the Hamiltonian's structure is available.