Reinforcement Learning in Different Phases of Quantum Control

Reinforcement Learning in Different Phases of Quantum Control

Blog Article

The ability to prepare a physical system in a desired quantum state is central to many areas of physics such as nuclear magnetic resonance, cold atoms, and quantum computing.Yet, preparing states quickly and with high fidelity remains a formidable challenge.In this work, we implement cutting-edge reinforcement learning (RL) techniques and show that their performance is comparable to optimal control methods in the task of finding short, high-fidelity Intakes driving protocol from an initial to a target state in nonintegrable many-body quantum systems of interacting qubits.

RL methods learn about the underlying physical system solely through a single scalar reward (the fidelity of the resulting state) calculated from numerical simulations of the physical system.We further show that quantum-state manipulation viewed as an optimization problem exhibits a spin-glass-like phase transition in the space of protocols as a function of the protocol duration.Our RL-aided approach helps identify variational protocols Yoto Card - Storytime with nearly optimal fidelity, even in the glassy phase, where optimal state manipulation is exponentially hard.

This study highlights the potential usefulness of RL for applications in out-of-equilibrium quantum physics.