Volterra filters for quantum estimation and detection

TL;DR

This paper introduces Volterra filters as a computationally efficient alternative for quantum estimation and detection in high-dimensional systems, applicable without Markovian assumptions.

Contribution

It proposes a hierarchy of Volterra filters for quantum inference, applicable to complex systems, and demonstrates their use in various quantum estimation tasks.

Findings

First-order Volterra filter applied to quantum filtering

Derived a Heisenberg-picture uncertainty relation

Applied to quantum state tomography and qubit readout

Abstract

The implementation of optimal statistical inference protocols for high-dimensional quantum systems is often computationally expensive. To avoid the difficulties associated with optimal techniques, here I propose an alternative approach to quantum estimation and detection based on Volterra filters. Volterra filters have a clear hierarchy of computational complexities and performances, depend only on finite-order correlation functions, and are applicable to systems with no simple Markovian model. These features make Volterra filters appealing alternatives to optimal nonlinear protocols for the inference and control of complex quantum systems. Applications of the first-order Volterra filter to continuous-time quantum filtering, the derivation of a Heisenberg-picture uncertainty relation, quantum state tomography, and qubit readout are discussed.