Experimental memory control in continuous variable optical quantum reservoir computing

TL;DR

This paper presents a scalable optical quantum reservoir computing platform using continuous-variable states, demonstrating enhanced memory and expressivity for nonlinear temporal tasks through multimode entanglement and multiplexing.

Contribution

It introduces a fully continuous-variable optical QRC system with spectral and temporal multiplexing, enabling controlled fading memory and improved performance on temporal tasks.

Findings

Achieved real-time and long-term memory via feedback and spatial multiplexing.

Demonstrated nonlinear temporal tasks like parity checking and chaotic forecasting.

Leveraged multimode entanglement to enhance reservoir expressivity.

Abstract

Quantum reservoir computing (QRC) offers a promising framework for online quantum-enhanced machine learning tailored to temporal tasks, yet practical implementations with native memory capabilities remain limited. Here, we demonstrate an optical QRC platform based on deterministically generated multimode squeezed states, exploiting both spectral and temporal multiplexing in a fully continuous-variable (CV) setting, and enabling controlled fading memory. Data is encoded via programmable phase shaping of the pump in an optical parametric process and retrieved through mode-selective homodyne detection. Real-time memory is achieved through feedback using electro-optic phase modulation, while long-term dependencies are achieved via spatial multiplexing. This architecture with minimal post-processing performs nonlinear temporal tasks, including parity checking and chaotic signal forecasting, with results corroborated by a high-fidelity Digital Twin. We show that leveraging the entangled multimode structure significantly enhances the expressivity and memory capacity of the quantum reservoir. This work establishes a scalable photonic platform for quantum machine learning, operating in CV encoding and supporting practical quantum-enhanced information processing.