连续变光量子库计算中的实验存储器控制
近日，法国索邦大学Valentina Parigi团队研究了连续变光量子库计算中的实验存储器控制。该项研究成果发表在2026年3月17日出版的《自然—光子学》杂志上。
预测复杂过程需要从时序数据中进行高效学习。储层计算平台能够以最小训练成本实现此类学习。量子储层计算（QRC）将该框架拓展至量子领域，为针对时序任务的在线量子增强机器学习提供了广阔前景。与经典情形类似，光子学为QRC提供了天然平台。然而，在实用光子量子系统中实现本征记忆能力仍是一项重大挑战。
研究组展示了一种基于确定性制备的多模压缩态的光子QRC平台，利用连续变量体系中具有可控渐逝记忆的频谱与时间复用技术。通过光学参量过程中的可编程泵浦相位整形实现数据编码，并采用模式选择零差探测进行信息读取。通过电光调制反馈实现实时记忆，并通过空间复用增强表达力。该架构能够执行非线性时序任务，包括不同延迟下的奇偶校验和混沌信号预测。所有结果均得到高保真度数字孪生的支持。利用纠缠多模结构可增强表达力与记忆容量，为量子增强信息处理建立了可扩展的连续变量光子平台。
附：英文原文
Title: Experimental memory control in continuous-variable optical quantum reservoir computing
Author: Paparelle, Iris, Henaff, Johan, Garca-Beni, Jorge, Gillet, milie, Montesinos, Daniel, Giorgi, Gian Luca, Soriano, Miguel C., Zambrini, Roberta, Parigi, Valentina
Issue&Volume: 2026-03-17
Abstract: Forecasting complex processes requires efficient learning from temporal data. Reservoir computing platforms enable such learning with minimal training cost. Quantum reservoir computing (QRC) extends this framework into the quantum domain, offering promising capabilities for online, quantum-enhanced machine learning tailored to temporal tasks. As in the classical case, photonics provides a natural platform for QRC. However, implementing native memory capabilities in practical photonic quantum systems remains a major challenge. Here we demonstrate a photonic QRC platform based on deterministically generated multimode squeezed states, exploiting spectral and temporal multiplexing in a continuous-variable setting with controllable fading memory. Data is encoded via programmable pump phase shaping in an optical parametric process and retrieved through mode-selective homodyne detection. Real-time memory is implemented through feedback via electro-optic modulation, and expressivity is boosted via spatial multiplexing. This architecture enables nonlinear temporal tasks, including parity check at different delays and chaotic signal forecasting. All results are supported by a high-fidelity Digital Twin. Leveraging the entangled multimode structure enhances expressivity and memory capacity, establishing a scalable continuous-variable photonic platform for quantum-enhanced information processing.
DOI: 10.1038/s41566-026-01880-9
Source: https://www.nature.com/articles/s41566-026-01880-9