Boson sampling has demonstrated quantum advantage yet struggled to find real world practical applications. We show these processors generate the complex dynamics necessary to power quantum reservoir computing and utilize it for various image recognition and classification tasks.
Quantum advantage has been demonstrated in a number of physical systems with boson sampling, a well-known restricted non-universal quantum computation model uniquely applicable to photonic systems, being one of the most significant examples. The boson sampling computational processors however has struggled to find real world practical applications they can be applied too. Here we show these processors can be used to perform various image recognition and classification tasks, even for modest sized systems. More specifically we show that these random interferometer-based boson sampling processors generate the complex dynamics necessary to power quantum reservoir computing.
It is well known that boson sampling, a restricted non-universal quantum computation model, enables one to perform tasks that are hard to accomplish with digital computers. Boson sampling is associated with sampling the probability distribution of identical bosons passing through a random interferometer, and its quantum advantage has been demonstrated. It has, however, proven elusive to use such a model for practical applications. In this work, we show that the random interferometer powering boson sampling can be used to generate the complex dynamics necessary for quantum reservoir computing. We use these dynamics to perform various image recognition problems, illustrating the utility of the approach even for modest-size systems.
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 reservoir computing has emerged as a promising paradigm within the field of quantum machine learning, harnessing the inherent properties of quantum systems to optimise and enhance information processing capabilities. Here, we explore the potential of quantum-inspired machine learning methodologies by leveraging the complex dynamics of quantum reservoirs to address computationally challenging tasks with enhanced efficiency and accuracy. To this end, we employ an open quantum system model comprising two-level atomic ensembles coupled to Lorentzian photonic cavities to construct a quantum physical reservoir computer layer for a recurrent neural network. We evaluate the effectiveness of this approach by applying it to a standard machine learning image-recognition problem and benchmarking its performance against a conventional neural network of similar architecture, but lacking the quantum physical reservoir computer layer. Remarkably, as the dataset size increases, the quantum physical reservoir computer outperforms the conventional neural network, requiring fewer training epochs and a smaller dataset to achieve comparable accuracy. Furthermore, we employ the quantum physical reservoir computing approach to model the dynamics of open quantum systems, focusing on atomic system ensembles interacting with a structured photonic reservoir associated with a photonic band-gap material. Our results reveal that the quantum reservoir computer provides equally powerful representations for quantum dynamical problems, maintaining effectiveness even under constraints of limited training data.
Integrated photonic microprocessors provide high-bandwidth, massively parallel linear computation, but realizing nonlinear feature maps and temporal memory remain key challenges for machine learning. Conventional approaches rely on active tuning and additional nonlinear elements, increasing architectural complexity and power overhead. Here we demonstrate an integrated photonic quantum reservoir computer that achieves nonlinear mapping, fading memory, and task versatility without active tuning of the reservoir core. The same chip supports accurate static classification, dynamic prediction, and stable autonomous forecasting, establishing broad utility across both classification and temporal inference tasks. Competitive performance is retained in the zero-bias state, where all on-chip phase shifters are unpowered, eliminating active control and reducing computational power consumption to zero. This passive operation highlights a scalable route to multifunctional machine-learning hardware, where large-scale photonic quantum processors can be repurposed as reservoirs without reconfiguring their internal optical networks. By combining quantum-state encoding, multimode interferometric mixing, and photon-statistical readout, this architecture provides a physically grounded paradigm for low-power, large-scale quantum reservoir computing.
Today's quantum processors composed of fifty or more qubits have allowed us to enter a computational era where the output results are not easily simulatable on the world's biggest supercomputers. What we have not seen yet, however, is whether or not the complexity achievable with such quantum processors would ever be useful for any practical applications. In the quantum neural network approach, there has been an expectation that the Hilbert space can serve as a computational resource by providing a large feature space; however, it is not understood, nor illustrated in what way the Hilbert space provides that computational power. In this work we introduce a resource-efficient quantum neural network model with a focus on the role of the feature space the quantum processors can give. Then we apply it for classification tasks, showing a quantum advantage in terms of the physical resources needed to realize the neural network. To facilitate the Hilbert space as a large feature space, we utilize scale-free networks that can be generated in a discrete-time crystal model. The virtue of this approach is in both its theoretical simplicity and its practical applicability: our quantum neural network model is simple enough to identify the role of the Hilbert space as the feature space, and, furthermore, can be implemented with current technology. Our model does not require optimization of the quantum processor, and hence once the quantum processor has been set, it can be used for other different classification problems without any changes in the quantum processor itself.