Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Understand

Quantum many-body control is a central milestone en route to harnessing quantum technologies.

• However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols.
• Here, we present a novel framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL).
• We tackle the quantum control problem by leveraging matrix product states (i) for representing the many-body state and, (ii) as part of the trainable machine learning architecture for our RL agent.