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Emerging Topics in Artificial Intelligence (ETAI)

Quantum reservoir computing with nonlinear optics: experimental continuous variable memory control

Iris Paparelle, Johan Henaff, Jorge García‐Beni, Roberta Zambrini, Valentina Parigi

Published Sep 17, 2025• 0 citations
Publisher

Abstract

Photonics quantum networks play a crucial role in quantum information processing by enabling efficient distribution and manipulation of quantum states of light. Femtosecond pulses, when combined with nonlinear interactions, generate entangled multimode outputs, which can be enhanced through mode-selective homodyne detection, forming quantum cluster states. These naturally arising nonlinearities and correlations, integrated with optical or electronic feedback, serve as a foundation for quantum reservoir computing (QRC). We present a scalable, room-temperature approach to quantum computing without single-photon sources or detectors. We report a proof-of-principle experimental realization of QRC using pump phase encoding with electronic feedback, with ongoing efforts focused on optimizing implementations through pulse shaping. Additionally, we explore a novel measurement-based QRC protocol using cluster states and teleportation, where measurements drive input encoding, processing, and time series monitoring.

DOI
10.1117/12.3063910

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Photonics quantum networks play a crucial role in quantum information processing by enabling efficient distribution and manipulation of quantum states of light. Femtosecond pulses, when combined with nonlinear interactions, generate entangled multimode outputs, which can be enhanced through mode-selective homodyne detection, forming quantum cluster states. These naturally arising nonlinearities and correlations, integrated with optical or electronic feedback, serve as a foundation for quantum reservoir computing (QRC). We present a scalable, room-temperature approach to quantum computing without single-photon sources or detectors. We report a proof-of-principle experimental realization of QRC using pump phase encoding with electronic feedback, with ongoing efforts focused on optimizing implementations through pulse shaping. Additionally, we explore a novel measurement-based QRC protocol using cluster states and teleportation, where measurements drive input encoding, processing, and time series monitoring.
Quantum reservoir computing (QRC) leverages the nonlinearity and correlations of quantum systems to enhance machine learning tasks. We explore continuous-variable (CV) photonic platforms for QRC using frequency- and pulse-multiplexed squeezed states generated via nonlinear waveguides, enabling scalable, room-temperature architectures without single-photon sources or detectors. We introduce a novel, measurement-based QRC protocol that uses a neural network of cluster states and local operations, in which input data are encoded through measurement via quantum teleportation. In this design, measurements enable input injection, information processing, and continuous monitoring for time-series tasks. The architecture’s power and versatility are demonstrated through benchmark tasks, showing that the protocol exhibits internal memory and is suitable for both static and temporal information processing without hardware modifications. Additionally, we report a proof-of-principle experimental realization of QRC based on pump-phase encoding and electronic feedback, demonstrating temporal data processing capabilities.
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.
Photonic quantum technologies are noteworthy candidates in the achievement of quantum advantage for quantum information processing. Moreover, their capabilities for fast signal processing have attracted the interest of researchers in the field of quantum reservoir computing (QRC). In our research, we propose a scalable quantum photonic platform for QRC suitable for solving temporal tasks. In our platform, an optical pulse recirculating through an optical cavity creates a quantum memory, thus not needing external classical storage. A classical signal is sequentially encoded in the quantum field fluctuations of external optical pulses, which interact with the cavity pulse using a beam-splitter (BS). A nonlinear crystal is placed inside the cavity to generate non-trivial dynamics and create a quantum network of entangled modes. A homodyne detector is placed at one of the output paths of the BS for sequential data collecting. Our work focuses on the ability to process classical signals in real time and the noise robustness of our architecture.
Quantum Reservoir Computing (QRC) leverages quantum systems to perform complex computational tasks with exceptional efficiency and reduced energy consumption. We introduce a minimalistic QRC framework utilizing as few as five atoms in a single-mode optical cavity, combined with continuous quantum measurement. The system is conveniently scalable, as newly added atoms naturally couple with existing ones via the shared cavity field. To achieve high computational expressivity with a minimal reservoir, we include two critical elements: reservoir feedback and polynomial regression. Reservoir feedback modifies the reservoir’s dynamics without altering its internal quantum hardware, while polynomial regression nonlinearly enhances output resolution. We demonstrate significant QRC performance in memory retention and nonlinear data processing through two tasks: predicting chaotic time-series data via the Mackey-Glass task and classifying sine-square waveforms. This framework fulfills QRC’s objectives to minimize hardware size and energy consumption, marking a significant advancement in integrating quantum physics with machine learning technology.
Abstract We propose a new approach for a photonic platform suitable for distributed quantum machine learning and exhibiting memory. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing as well as quantum communication protocols. The proposed measurement-based quantum reservoir computing is based on a neural network of cluster states and local operations, where input data are encoded through measurement, thanks to quantum teleportation. In this design, measurements enable input injections, information processing and continuous monitoring for time series processing. The architecture’s power and versatility are tested by performing a set of benchmark tasks showing that the protocol displays internal memory and is suitable for both static and temporal information processing without hardware modifications. This design opens the way to distributed machine learning.
Continuous variable encoding of quantum information requires the deterministic generation of highly correlated quantum states of light in the form of quantum networks, which, in turn, necessitates the controlled generation of a large number of squeezed modes. In this paper, we present an experimental source of multimode squeezed states of light at telecommunication wavelengths. Generation at such wavelengths is especially important as it can enable quantum information processing, communication, and sensing beyond the laboratory scale. We use a single-pass spontaneous parametric down-conversion process in a nonlinear waveguide pumped with the second harmonic of a femtosecond laser. Our measurements reveal significant squeezing in more than 21 frequency modes, with a maximum squeezing value exceeding 2.5 dB. We demonstrate multiparty entanglement by measuring the state's covariance matrix. Finally, we show the source reconfigurability by preparing few-node cluster states and measure their nullifier squeezing level. These results pave the way for a scalable implementation of continuous variable quantum information protocols at telecommunication wavelengths, particularly for multiparty, entanglement-based quantum communications. Moreover, the source is compatible with additional pulse-by-pulse multiplexing, which can be utilized to construct the necessary three-dimensional entangled structures for quantum computing protocols. Published by the American Physical Society 2024