Adaptive schemes for incomplete quantum process tomography

TL;DR

This paper introduces an iterative, adaptive algorithm for incomplete quantum process tomography that efficiently estimates unknown quantum processes using less data by combining maximum-likelihood and maximum-entropy principles.

Contribution

It presents a novel iterative algorithm that optimizes quantum process estimation with incomplete data, reducing resource requirements compared to traditional methods.

Findings

Algorithm provides a unique estimator with incomplete data.

Adaptive application improves resource efficiency.

Combines maximum-likelihood and maximum-entropy principles.

Abstract

We propose an iterative algorithm for incomplete quantum process tomography, with the help of quantum state estimation, based on the combined principles of maximum-likelihood and maximum-entropy. The algorithm yields a unique estimator for an unknown quantum process when one has less than a complete set of linearly independent measurement data to specify the quantum process uniquely. We apply this iterative algorithm adaptively in various situations and so optimize the amount of resources required to estimate the quantum process with incomplete data.