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. Researchers demonstrate a photonic quantum reservoir computing platform that uses spectral and temporal multiplexing in a continuous-variable setting. Real-time memory is implemented, and nonlinear temporal tasks are enabled.

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Nature

Researchers demonstrate a photonic quantum reservoir computing (QRC) platform using deterministically generated multimode squeezed states with spectral and temporal multiplexing in a continuous-variable setting. The system implements controllable fading memory via real-time electro-optic feedback, encodes data through programmable pump phase shaping, and retrieves it via mode-selective homodyne detection. Expressivity is enhanced through spatial multiplexing and by exploiting the entangled multimode structure. The platform successfully performs nonlinear temporal tasks including XOR, parity check at various delays, and chaotic signal forecasting (double-scroll, Lorenz). A high-fidelity Digital Twin accurately reproduces experimental results. The architecture achieves polynomial scaling of expressivity with measured modes and outperforms purely spatial multiplexing strategies, establishing a scalable room-temperature photonic platform for quantum-enhanced temporal information processing.

Experimental memory control in continuous-variable optical quantum reservoir computing

Information encoding and memory control of the quantum reservoir