Estimation of general Hamiltonian parameters via controlled energy measurements

TL;DR

This paper investigates how controlled energy measurements can outperform traditional quantum Fisher information bounds in estimating general Hamiltonian parameters, especially when the parameter influences the energy spectrum non-linearly.

Contribution

It introduces and optimizes controlled energy measurements for general Hamiltonian parameters, showing they can surpass standard quantum bounds and detailing practical implementation methods.

Findings

Controlled energy measurements can outperform Braunstein-Caves measurements.

Optimal performance depends on the non-linear dependence of the Hamiltonian on the parameter.

The paper provides a realistic implementation approach using quantum phase estimation.

Abstract

The quantum Cram\'er-Rao theorem states that the quantum Fisher information (QFI) bounds the best achievable precision in the estimation of a quantum parameter $\xi$. This is true, however, under the assumption that the measurement employed to extract information on $\xi$ are regular, i.e. neither its sample space nor its positive-operator valued elements depend on the (true) value of the parameter. A better performance may be achieved by relaxing this assumption. In the case of a general Hamiltonian parameter, i.e. when the parameter enters the system's Hamiltonian in a non-linear way (making the energy eigenstates and eigenvalues $\xi$-dependent), a family of non-regular measurements, referred to as controlled energy measurements, is naturally available. We perform an analytic optimization of their performance, which enables us to compare the optimal controlled energy measurement with the optimal Braunstein-Caves measurement based on the symmetric logarithmic derivative. As the former may outperform the latter, the ultimate quantum bounds for general Hamiltonian parameters are different than those for phase (shift) parameters. We also discuss in detail a realistic implementation of controlled energy measurements based on the quantum phase estimation algorithm and work out a variety of examples to illustrate our results.