Adaptive Bayesian Quantum Tomography

TL;DR

This paper introduces an adaptive Bayesian framework for quantum tomography that dynamically optimizes measurements based on collected data, significantly reducing the number of measurements needed compared to traditional methods.

Contribution

It presents a novel adaptive approach to quantum tomography using Bayesian inference, enabling real-time measurement optimization and improved efficiency.

Findings

Ten-fold reduction in measurements compared to non-adaptive methods

Effective adaptive re-optimization based on Bayesian inference

Enhanced efficiency over mutually unbiased bases

Abstract

In this letter we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance of the experiment, we allow for measurements to be adaptively and efficiently re-optimised depending on data collected so far. We develop an adaptive statistical framework based on Bayesian inference and Shannon's information, and demonstrate a ten-fold reduction in the total number of measurements required as compared to non-adaptive methods, including mutually unbiased bases.