Two-Qubit Hamiltonian Tomography by Bayesian Analysis of Noisy Data

TL;DR

This paper introduces a Bayesian-based method for two-qubit Hamiltonian tomography using minimal measurement bases, enabling accurate reconstruction of the system's dynamics even with noisy data.

Contribution

It presents a novel empirical approach for Hamiltonian tomography that requires only fixed-basis measurements and extends to controllable systems with a multi-step process.

Findings

Effective Hamiltonian reconstruction from noisy data

Method works with limited measurement basis

Achieves full control Hamiltonian tomography

Abstract

We present an empirical strategy to determine the Hamiltonian dynamics of a two-qubit system using only initialization and measurement in a single fixed basis. Signal parameters are estimated from measurement data using Bayesian methods from which the underlying Hamiltonian is reconstructed, up to three unobservable phase factors. We extend the method to achieve full control Hamiltonian tomography for controllable systems via a multi-step approach. The technique is demonstrated and evaluated by analyzing data from simulated experiments including projection noise.