Exploring Model-Free Reinforcement Learning for Quantum Sensing

Deep learning for quantum sensors

Quantum sensing is one of the most promising uses of quantum technology, with the goal of improving measurement sensitivity by using quantum resources. Sensing optical phases, in particular, is considered one of the most important problems for developing mass-produced technology devices.

For optimal use of quantum sensors, regular calibration and characterization is required. This calibration can be a resource-intensive and complex task, especially when it comes to systems that estimate multiple parameters. Machine-learning algorithms are a powerful way to deal with this complexity. It is crucial to develop sensors that can perform precise quantum-enhanced measurement by discovering the best algorithms.

The \”reinforcement-learning\” algorithm (RL) is a type of machine learning algorithm that relies on a reward-driven intelligent agent. It learns the best actions to take to reach the optimization desired based on the rewards received. Recent reports have reported the first experiments using RL algorithms to optimize quantum problems. The majority of them rely on the prior knowledge of the system model. It is better to have a model-free approach. This is only possible when the reward of the agent does not depend on the system model.

Source:
https://phys.org/news/2023-02-deep-quantum.html