分布式量子特征提取揭示OAM编码量子比特中的结构化泄漏

中文摘要 AI 辅助

轨道角动量（OAM）模式已成为高维量子通信的重要平台，但仍易受环境噪声和信道缺陷的影响。本文提出一种OAM传播的逻辑量子比特表示，其中模式通过噪声量子信道时以量子比特形式演化。在该表示中，湍流表现为从逻辑OAM子空间泄漏到湍流诱导的泄漏分量，为单光子和相干光束 regime 中的退化提供了统一描述。研究表明，湍流诱导的泄漏对湍流强度和OAM阶数具有系统性依赖，揭示了超出逻辑子空间的信息重分布的结构化特征。利用这一见解，开发了分布式量子特征提取方案，该方案将高维OAM态编码为量子表示，并采用量子卷积操作提取抗湍流特征。模拟和实验测量表明，在宽范围的湍流强度、OAM阶数和传播条件下，均可实现这些特征的鲁棒提取。研究结果建立了OAM信道动力学、结构化泄漏与量子学习之间的直接联系，为表征湍流退化的高维光学态及提取湍流诱导信息重分布的鲁棒特征提供了物理信息驱动的框架。

英文摘要

Orbital angular momentum (OAM) modes have emerged as an important platform for high-dimensional quantum communication but remain vulnerable to environmental noise and channel imperfections. Here we introduce a logical-qubit representation of OAM propagation in which modes evolve as qubits through noisy quantum channels. In this representation, turbulence manifests as leakage from the logical OAM subspace into turbulence-induced leakage components, providing a unified description of degradation in both single-photon and coherent-beam regimes. We show that turbulence-induced leakage exhibits systematic dependence on turbulence strength and OAM order, revealing structured signatures of information redistribution beyond the logical subspace. Leveraging this insight, we develop a distributed quantum feature-extraction scheme that encodes high-dimensional OAM states into quantum representations and employs quantum convolutional operations to extract turbulence-robust features. Simulations and experimental measurements demonstrate robust extraction of these features across a broad range of turbulence strengths, OAM orders, and propagation conditions. Our results establish a direct link between OAM-channel dynamics, structured leakage, and quantum learning, providing a physics-informed framework for characterizing turbulence-degraded high-dimensional optical states and extracting robust signatures of turbulence-induced information redistribution.

发表机构

• Southern Methodist University（南方卫理公会大学）
• University of Illinois Chicago（伊利诺伊大学芝加哥分校）

机构由 AI 辅助整理，请以论文原文为准。