We construct an algorithm that learns through trial-and-error training how to devise optimal feedback-based single-shot phase estimation in interferometry. Our algorithm is robust against experimental imperfections, losses and decoherence.
Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.
One of the most immediate practical applications of quantum information processing is performing precise quantum measurements. Quantum measurement schemes employing adaptive feedback are most effective, since accumulated information from measurements is exploited to maximize the information gain in subsequent measurements. Yet devising such feedback policies is complicated and often involves clever guesswork. Here we present an automated method, based on machine learning, to generate adaptive feedback measurement policies. We apply our technique to adaptive quantum phase measurement, which is important for applications such as atomic clocks and gravitational wave detection. Our algorithm autonomously learns to perform phase estimation based on experimental trial runs, which can be either simulated or performed using a real world experiment.