Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements

TL;DR

This paper demonstrates that deep reinforcement learning can discover effective feedback strategies for quantum state preparation, including stabilizing Fock and superposition states, using nonlinear measurements without prior knowledge.

Contribution

It shows that reinforcement learning can find feedback control strategies for quantum state preparation with nonlinear measurements, a task previously difficult due to the complex search space.

Findings

Reinforcement learning successfully stabilizes Fock states at high fidelity.

It enables the creation of superposition states by controlling measurement rates.

The approach does not require prior knowledge of the quantum system.

Abstract

Quantum control has been of increasing interest in recent years, e.g. for tasks like state initialization and stabilization. Feedback-based strategies are particularly powerful, but also hard to find, due to the exponentially increased search space. Deep reinforcement learning holds great promise in this regard. It may provide new answers to difficult questions, such as whether nonlinear measurements can compensate for linear, constrained control. Here we show that reinforcement learning can successfully discover such feedback strategies, without prior knowledge. We illustrate this for state preparation in a cavity subject to quantum-non-demolition detection of photon number, with a simple linear drive as control. Fock states can be produced and stabilized at very high fidelity. It is even possible to reach superposition states, provided the measurement rates for different Fock states can be controlled as well.