Machine Learning for Precise Quantum Measurement

TL;DR

This paper introduces a machine learning framework to autonomously generate adaptive feedback schemes for quantum measurements, outperforming existing methods in interferometric phase estimation.

Contribution

It adapts machine learning to quantum measurement, creating fully-automatic, programmable feedback schemes that improve upon traditional adaptive measurement strategies.

Findings

Generated schemes outperform the best known adaptive scheme in interferometric phase estimation.

The framework automates the design of quantum measurement algorithms.

Demonstrates the potential of machine learning in quantum information processing.

Abstract

Adaptive feedback schemes are promising for quantum-enhanced measurements yet are complicated to design. Machine learning can autonomously generate algorithms in a classical setting. Here we adapt machine learning for quantum information and use our framework to generate autonomous adaptive feedback schemes for quantum measurement. In particular our approach replaces guesswork in quantum measurement by a logical, fully-automatic, programmable routine. We show that our method yields schemes that outperform the best known adaptive scheme for interferometric phase estimation.