Dynamical learning of a photonics quantum-state engineering process

TL;DR

This paper presents an automated adaptive optimization protocol for engineering high-dimensional photonic quantum states, specifically OAM states, in a black-box experimental setup, improving robustness and applicability in noisy conditions.

Contribution

The authors develop and experimentally demonstrate a fully black-box adaptive optimization scheme for quantum state engineering that does not require detailed knowledge of the apparatus.

Findings

Successfully engineered four-dimensional OAM states using the protocol.

The method is robust against external perturbations.

Applicable to both classical and quantum regimes.

Abstract

Experimentally engineering high-dimensional quantum states is a crucial task for several quantum information protocols. However, a high degree of precision in the characterization of experimental noisy apparatus is required to apply existing quantum state engineering protocols. This is often lacking in practical scenarios, affecting the quality of the engineered states. Here, we implement experimentally an automated adaptive optimization protocol to engineer photonic Orbital Angular Momentum (OAM) states. The protocol, given a target output state, performs an online estimation of the quality of the currently produced states, relying on output measurement statistics, and determines how to tune the experimental parameters to optimize the state generation. To achieve this, the algorithm needs not be imbued with a description of the generation apparatus itself. Rather, it operates in a fully black-box scenario, making the scheme applicable in a wide variety of circumstances. The handles controlled by the algorithm are the rotation angles of a series of waveplates and can be used to probabilistically generate arbitrary four-dimensional OAM states. We showcase our scheme on different target states both in classical and quantum regimes, and prove its robustness to external perturbations on the control parameters. This approach represents a powerful tool for automated optimizations of noisy experimental tasks for quantum information protocols and technologies.