Experimental adaptive process tomography

TL;DR

This paper extends adaptive Bayesian methods from quantum state to process tomography, demonstrating experimentally that adaptive measurements improve the precision of learning unknown quantum operations, especially under technical noise.

Contribution

It generalizes adaptive Bayesian process tomography and experimentally verifies its advantage over non-adaptive methods for single-qubit channels.

Findings

Adaptive tomography lowers noise floor under technical noise.

Experimental validation across all single-qubit channels.

Provides criteria for ultimate achievable precision.

Abstract

Adaptive measurements were recently shown to significantly improve the performance of quantum state tomography. Utilizing information about the system for the on-line choice of optimal measurements allows to reach the ultimate bounds of precision for state reconstruction. In this article we generalize an adaptive Bayesian approach to the case of process tomography and experimentally show its superiority in the task of learning unknown quantum operations. Our experiments with photonic polarization qubits cover all types of single-qubit channels. We also discuss instrumental errors and the criteria for evaluation of the ultimate achievable precision in an experiment. It turns out, that adaptive tomography provides a lower noise floor in the presence of strong technical noise.