Benchmarking Machine Learning Algorithms for Adaptive Quantum Phase Estimation with Noisy Intermediate-Scale Quantum Sensors

TL;DR

This paper demonstrates how classical machine learning algorithms like Differential Evolution and Particle Swarm Optimization can optimize adaptive quantum phase estimation, improving precision in noisy quantum sensors.

Contribution

It introduces a benchmarking framework for adaptive quantum phase estimation using ML algorithms, highlighting their robustness against various noise sources.

Findings

ML-based adaptive schemes outperform non-adaptive methods under noise

Optimal feedback policies minimize Holevo variance effectively

Robustness demonstrated across different quantum sensing platforms

Abstract

Quantum phase estimation is a paradigmatic problem in quantum sensing andmetrology. Here we show that adaptive methods based on classical machinelearning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach-Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions,superconducting qubits and nitrogen-vacancy (NV) centers in diamond.