Machine learning for efficient generation of universal hybrid quantum computing resources

TL;DR

This paper demonstrates that deep reinforcement learning applied to a measurement-based quantum processor can efficiently generate high-quality quantum states with a success rate of 98%, surpassing previous methods.

Contribution

The study introduces a novel application of deep reinforcement learning to optimize quantum state generation in measurement-based quantum computing.

Findings

Achieved 98% success rate in generating squeezed cat states.

Outperformed all similar existing proposals in quantum state generation.

Validated the effectiveness of reinforcement learning in quantum resource preparation.

Abstract

We present numerical simulations of deep reinforcement learning on a measurement-based quantum processor--a time-multiplexed optical circuit sampled by photon-number-resolving detection--and find it generates squeezed cat states with an average success rate of 98%, far outperforming all other similar proposals.