Reconstruction of a Photonic Qubit State with Reinforcement Learning

TL;DR

This paper demonstrates a reinforcement learning method for reconstructing unknown photonic qubit states with high fidelity using limited quantum copies, advancing quantum state tomography techniques.

Contribution

It introduces a semi-quantum reinforcement learning approach for photonic qubit state reconstruction, achieving over 88% fidelity within 50 iterations.

Findings

Achieved over 88% fidelity in state reconstruction

Effective with limited quantum copies

Applicable to higher-dimensional and mixed states

Abstract

An experiment is performed to reconstruct an unknown photonic quantum state with a limited amount of copies. A semi-quantum reinforcement learning approach is employed to adapt one qubit state, an "agent," to an unknown quantum state, an "environment," by successive single-shot measurements and feedback, in order to achieve maximum overlap. The experimental learning device herein, composed of a quantum photonics setup, can adjust the corresponding parameters to rotate the agent system based on the measurement outcomes "0" or "1" in the environment (i.e., reward/punishment signals). The results show that, when assisted by such a quantum machine learning technique, fidelities of the deterministic single-photon agent states can achieve over 88% under a proper reward/punishment ratio within 50 iterations. This protocol offers a tool for reconstructing an unknown quantum state when only limited copies are provided, and can also be extended to higher dimensions, multipartite, and mixed quantum state scenarios.