Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, while efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed, random optical network for photonic quantum machine learning in the specific form of a reservoir computer that is enabled by photon number-resolved detection of the output states. This provides access to a combinatorially scaling Hilbert space, while using significantly simpler quantum states than comparable quantum machine learning approaches. The approach is implementable with currently available technology and lowers the entry barrier for quantum machine learning.Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, while efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed, random optical network for photonic quantum machine learning in the specific form of a reservoir computer that is enabled by photon number-resolved detection of the output states. This provides access to a combinatorially scaling Hilbert space, while using significantly simpler quantum states than comparable quantum machine learning approaches. The approach is implementable with currently available technology and lowers the entry barrier for quantum machine learning.

Optica QuantumApr 1, 202511 citations

Quantum Light Detection with Enhanced Photonic Neural Network

Stanisław Świerczewski, Dogyun Ko, Amir Rahmani +7 more

Advances in quantum technologies are accelerating the demand for optical quantum state sensors that combine high precision, versatility, and scalability within a unified hardware platform. Quantum reservoir computing offers a powerful route toward this goal by exploiting the nonlinear dynamics of quantum systems to process and interpret quantum information efficiently. Photonic neural networks are particularly well suited for such implementations, owing to their intrinsic sensitivity to photon-encoded quantum information. However, the practical realisation of photonic quantum reservoirs remains constrained by the inherently weak optical nonlinearities of available materials and the technological challenges of fabricating densely coupled quantum networks. To address these limitations, we introduce a hybrid quantum-classical detection protocol that integrates the advantages of quantum reservoirs with the adaptive learning capabilities of analogue neural networks. This synergistic architecture substantially enhances information-extraction accuracy and robustness, enabling low-cost performance improvements of quantum light sensors. Based on the proposed approach, we achieved significant improvements in quantum state classification, tomography, and feature regression, even for reservoirs with a relatively small nonlinearity-to-losses ratio $U/γ\approx 0.02$ in a network of only five nodes. By reducing reliance on material nonlinearity and reservoir size, the proposed approach facilitates the practical deployment of high-fidelity photonic quantum sensors on existing integrated platforms, paving the way toward chip-scale quantum processors and photonic sensing technologies.Advances in quantum technologies are accelerating the demand for optical quantum state sensors that combine high precision, versatility, and scalability within a unified hardware platform. Quantum reservoir computing offers a powerful route toward this goal by exploiting the nonlinear dynamics of quantum systems to process and interpret quantum information efficiently. Photonic neural networks are particularly well suited for such implementations, owing to their intrinsic sensitivity to photon-encoded quantum information. However, the practical realisation of photonic quantum reservoirs remains constrained by the inherently weak optical nonlinearities of available materials and the technological challenges of fabricating densely coupled quantum networks. To address these limitations, we introduce a hybrid quantum-classical detection protocol that integrates the advantages of quantum reservoirs with the adaptive learning capabilities of analogue neural networks. This synergistic architecture substantially enhances information-extraction accuracy and robustness, enabling low-cost performance improvements of quantum light sensors. Based on the proposed approach, we achieved significant improvements in quantum state classification, tomography, and feature regression, even for reservoirs with a relatively small nonlinearity-to-losses ratio $U/γ\approx 0.02$ in a network of only five nodes. By reducing reliance on material nonlinearity and reservoir size, the proposed approach facilitates the practical deployment of high-fidelity photonic quantum sensors on existing integrated platforms, paving the way toward chip-scale quantum processors and photonic sensing technologies.

quant-pharXiv:2601.19721Jan 28, 20260 citations

Experimental quantum memristor

Michele Spagnolo, Joshua Morris, Simone Piacentini +6 more

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

arXiv:2105.04867May 11, 20215 citations

Quantum and classical processing with photonic quantum machine learning

J. C. López Carreño, S. Świerczewski, A. Opala +3 more

Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep quantum regime could not only lead to faster information processing, but also to perform tasks that are out of reach for classical systems. Here, we report a quantum reservoir processing device capable of performing both quantum and classical machine learning tasks. The implementation is realized with a programmable silicon chip excited with single photons, a highly scalable and adaptable photonics technology. We successfully implement a variety of quantum tasks, including quantum state tomography and measurement of entanglement via negativity. Moreover, we implement a method of mitigation of experimental imperfections which results in a significant improvement in accuracy in comparison to the same system operating in the classical regime. Our results demonstrate a method to overcome a crucial bottleneck of quantum technologies by providing a practical way of probing quantum states.Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep quantum regime could not only lead to faster information processing, but also to perform tasks that are out of reach for classical systems. Here, we report a quantum reservoir processing device capable of performing both quantum and classical machine learning tasks. The implementation is realized with a programmable silicon chip excited with single photons, a highly scalable and adaptable photonics technology. We successfully implement a variety of quantum tasks, including quantum state tomography and measurement of entanglement via negativity. Moreover, we implement a method of mitigation of experimental imperfections which results in a significant improvement in accuracy in comparison to the same system operating in the classical regime. Our results demonstrate a method to overcome a crucial bottleneck of quantum technologies by providing a practical way of probing quantum states.

quant-pharXiv:2605.10471May 12, 20260 citations

Modular photonic quantum reservoir computation for image recognition

William J. Munro, Akitada Sakurai, Aoi Hayashi +2 more

We present a modular photonic approach to quantum reservoir computing, leveraging complex optical dynamics for efficient machine learning. Using architectures inspired by boson sampling, we show that random photonic interferometers can serve as powerful quantum reservoirs for tasks such as image classification. Modular connectivity—achieved through long-range and inter-module photonic links—enables scalable and high-performance learning while remaining compatible with integrated photonic platforms. Additionally, we propose a resource-efficient quantum neural network model that uses scale-free photonic connectivity to exploit the Hilbert space as a rich feature space. Our approach requires no optimization, supporting general-purpose use on fixed photonic hardware.We present a modular photonic approach to quantum reservoir computing, leveraging complex optical dynamics for efficient machine learning. Using architectures inspired by boson sampling, we show that random photonic interferometers can serve as powerful quantum reservoirs for tasks such as image classification. Modular connectivity—achieved through long-range and inter-module photonic links—enables scalable and high-performance learning while remaining compatible with integrated photonic platforms. Additionally, we propose a resource-efficient quantum neural network model that uses scale-free photonic connectivity to exploit the Hilbert space as a rich feature space. Our approach requires no optimization, supporting general-purpose use on fixed photonic hardware.

Quantum Computing, Communication, and SimulationMar 5, 20260 citations

Enhancing the performance of quantum reservoir computing and solving the time-complexity problem by artificial memory restriction

Saud Čindrak, Brecht Donvil, Kathy Lüdge +1 more

We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing. Quantum reservoir computing is a computing approach which aims at utilizing the complexity and high dimensionality of small quantum systems, together with the fast trainability of reservoir computing, in order to solve complex tasks. The suitability of quantum reservoir computing for solving temporal tasks is hindered by the collapse of the quantum system when measurements are made. This leads to the erasure of the memory of the reservoir. Hence, for every output, the entire input signal is needed to reinitialize the reservoir, leading to quadratic time complexity. Another critical issue for the hardware implementation of quantum reservoir computing is the need for an experimentally accessible means of tuning the nonlinearity of the quantum reservoir. We present an approach which addresses both of these issues. We propose artificially restricting the memory of the quantum reservoir by only using a small number inputs to reinitialize the reservoir after measurements are performed. This strongly influences the nonlinearity of the reservoir response due to the influence of the initial reservoir state, while also substantially reducing the number of quantum operations needed to perform time-series prediction tasks due to the linear rather than quadratic time complexity. The reinitialization length therefore provides an experimental accessible means of tuning the nonlinearity of the response of the reservoir, which can lead to significant task-specific performance improvement. We numerically study the linear and quadratic algorithms for a fully connected transverse Ising model and a quantum processor model. Published by the American Physical Society 2024We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing. Quantum reservoir computing is a computing approach which aims at utilizing the complexity and high dimensionality of small quantum systems, together with the fast trainability of reservoir computing, in order to solve complex tasks. The suitability of quantum reservoir computing for solving temporal tasks is hindered by the collapse of the quantum system when measurements are made. This leads to the erasure of the memory of the reservoir. Hence, for every output, the entire input signal is needed to reinitialize the reservoir, leading to quadratic time complexity. Another critical issue for the hardware implementation of quantum reservoir computing is the need for an experimentally accessible means of tuning the nonlinearity of the quantum reservoir. We present an approach which addresses both of these issues. We propose artificially restricting the memory of the quantum reservoir by only using a small number inputs to reinitialize the reservoir after measurements are performed. This strongly influences the nonlinearity of the reservoir response due to the influence of the initial reservoir state, while also substantially reducing the number of quantum operations needed to perform time-series prediction tasks due to the linear rather than quadratic time complexity. The reinitialization length therefore provides an experimental accessible means of tuning the nonlinearity of the response of the reservoir, which can lead to significant task-specific performance improvement. We numerically study the linear and quadratic algorithms for a fully connected transverse Ising model and a quantum processor model. Published by the American Physical Society 2024

arXiv:2306.12876Physical Review ResearchJan 16, 202421 citations

U/γ\approx 0.02

in a network of only five nodes. By reducing reliance on material nonlinearity and reservoir size, the proposed approach facilitates the practical deployment of high-fidelity photonic quantum sensors on existing integrated platforms, paving the way toward chip-scale quantum processors and photonic sensing technologies.