The growing demand for efficient machine learning has motivated the exploration of neuro-morphic and reservoir computing, which are rooted in brain-inspired approaches that mimic the recurrent dynamics and adaptive information processing of biological neural circuits. These methods leverage the intrinsic dynamics of physical systems to process temporal in-formation with minimal training. Extending these ideas, Quantum Reservoir Computing (QRC) seeks to exploit uniquely quantum features—such as coherence, entanglement, and nonlinearity—for enhanced performance. Yet, the precise role of quantum resources in QRC remains unclear. This thesis investigates entanglement as a computational resource in QRC, focusing on a model of two coupled Kerr nonlinear oscillators. The Kerr platform is well suited for time-series prediction tasks due to its nonlinear interactions and access to a large Hilbert space. Using logarithmic negativity to quantify entanglement and normalized root mean square error (NRMSE) to measure predictive accuracy, we evaluate the effect of input drive strength, Kerr nonlinearity, and oscillator coupling on reservoir performance. Our results show that entanglement is related to improved computational accuracy on average and in worst-case scenarios, though it does not reduce the best-case error. The advantage persists under realistic imperfections: dissipation can enhance performance in certain regimes, while moderate dephasing does not completely remove the benefit. This thesis situates QRC within the context of neuromorphic and reservoir computing. It demonstrates the computational role of entanglement in a physically motivated QRC model and evaluates how key physical parameters affect performance under realistic conditions. This work advances understanding of whether and how quantum phenomena can be har-nessed for efficient learning, paving the way for high-performance quantum machine learning and time-series forecasting.
The growing demand for efficient machine learning has motivated the exploration of neuro-morphic and reservoir computing, which are rooted in brain-inspired approaches that mimic the recurrent dynamics and adaptive information processing of biological neural circuits. These methods leverage the intrinsic dynamics of physical systems to process temporal in-formation with minimal training. Extending these ideas, Quantum Reservoir Computing (QRC) seeks to exploit uniquely quantum features—such as coherence, entanglement, and nonlinearity—for enhanced performance. Yet, the precise role of quantum resources in QRC remains unclear. This thesis investigates entanglement as a computational resource in QRC, focusing on a model of two coupled Kerr nonlinear oscillators. The Kerr platform is well suited for time-series prediction tasks due to its nonlinear interactions and access to a large Hilbert space. Using logarithmic negativity to quantify entanglement and normalized root mean square error (NRMSE) to measure predictive accuracy, we evaluate the effect of input drive strength, Kerr nonlinearity, and oscillator coupling on reservoir performance. Our results show that entanglement is related to improved computational accuracy on average and in worst-case scenarios, though it does not reduce the best-case error. The advantage persists under realistic imperfections: dissipation can enhance performance in certain regimes, while moderate dephasing does not completely remove the benefit. This thesis situates QRC within the context of neuromorphic and reservoir computing. It demonstrates the computational role of entanglement in a physically motivated QRC model and evaluates how key physical parameters affect performance under realistic conditions. This work advances understanding of whether and how quantum phenomena can be har-nessed for efficient learning, paving the way for high-performance quantum machine learning and time-series forecasting.
Quantum Reservoir Computing (QRC) utilizes the intrinsic dynamics of quantum systems to process temporal data efficiently, offering potential advantages in computational power and energy efficiency. Unlike classical reservoirs, quantum reservoirs leverage properties such as superposition and entanglement, which may enhance information processing capabilities. In this work, we investigate a QRC framework based on two coupled Kerr nonlinear oscillators, a system well-suited for time-series prediction tasks due to its complex nonlinear interactions and potentially high-dimensional state space. We explore how its performance in time-series prediction depends on key physical parameters: input drive strength, Kerr nonlinearity, and oscillator coupling, and analyze the role of entanglement in improving the reservoir’s computational performance, focusing on its effect on predicting non-trivial time series. Using logarithmic negativity to quantify entanglement and normalized root mean square error (NRMSE) to evaluate predictive accuracy, our results suggest that entanglement provides a computational advantage on average up to a threshold in the input frequency, which persists under some levels of dissipation and dephasing. These findings contribute to the broader understanding of quantum reservoirs and highlight their potential for high performance, efficient quantum machine learning and time-series forecasting.
The intersection of quantum information processing and machine learning has recently at-tracted considerable interest within the physics and artificial intelligence communities. From a physical perspective, quantum computing offers fundamentally new modes of information processing, enabled by superposition, interference, and entanglement, which can lead to ad-vantages in computational speed and resource efficiency over classical approaches. These developments have motivated the exploration of machine learning paradigms that lever-age quantum dynamics as a computational resource. In parallel, reservoir computing—a non–von Neumann and neuromorphic computing framework—has gained attention due to its reliance on high-dimensional dynamical systems, in-memory computation, and low en-ergy consumption. Quantum reservoir computing extends this paradigm by exploiting the intrinsic dynamics of quantum many-body systems, such as spin networks, to process tem-poral information. Despite growing interest in quantum reservoir architectures, the role of genuinely quantum resources, in particular entanglement, in determining their information-processing capabilities remains incompletely understood. In this thesis, we study a distributed spin-network reservoir in which two independent time series are injected into separate qubits. Using short-term memory capacity as an opera-tional measure, we analyze how information introduced at distinct sites propagates through the network and how different entanglement structures contribute to storing and combining past inputs. Focusing on bilinear memory tasks, which require reconstructing the product of the two inputs at various delays, we probe temporal memory. Due to the computational cost associated with simulating open quantum many-body dynamics, the present study focuses on a minimal spin-network architecture consisting of four qubits, with two qubits serving as input nodes that receive independent time series. Even for such moderate system sizes, the numerical integration of the open-system dynamics are computationally demanding. Nevertheless, this minimal setting already captures the essential mechanisms of distributed information processing in quantum reservoirs. By computing the logarithmic negativity across different bipartitions of the reservoir, we investigate how memory performance relates to the underlying entanglement structure. We find that the short-term memory capacity is maximized at relatively weak coupling strengths, where entanglement is present but remains moderate. Among the various bipar-titions, the entanglement between the two input qubits consistently plays the most promi-nent role in learning performance. In this optimal regime, the reservoir exhibits long-lived memory, while a characteristic reduction in performance at zero delay, observed across all frequencies, reflects the finite time required for information to propagate through the sys-tem. Taken together, these results indicate that effective learning in distributed quantum reservoir computing is enabled by moderate, well-structured entanglement, rather than by maximal correlations.
We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its dispersive limit (DJC). These models provide high-dimensional Hilbert spaces and intrinsic nonlinear dynamics, making them powerful substrates for temporal information processing. We systematically benchmark both reservoirs through linear and nonlinear memory tasks, demonstrating that they exhibit an unusual superior nonlinear over linear memory capacity. We further test their predictive performance on the Mackey-Glass time series, a widely used benchmark for chaotic dynamics, and show comparable forecasting ability. We also investigate how memory and prediction accuracy vary with reservoir parameters and show the role of higher-order bosonic observables and time multiplexing in enhancing expressivity, even in minimal spin-boson configurations. Our results establish JC- and DJC-based reservoirs as versatile platforms for time-series processing and as elementary units that overcome the setting of equivalent qubit pairs and offer pathways toward tunable, high-performance quantum machine learning architectures.
This paper investigates the application of quantum reservoir computing (QRC) to time series prediction. Classical reservoir computing (RC) utilizes a complex, high-dimensional system – the 'reservoir' – as a non-linear feature extractor, followed by a linear regression model for prediction. QRC extends this concept by leveraging the inherent dynamics of a quantum system as the reservoir. We hypothesize that quantum systems, due to their superposition and entanglement properties, can provide richer and more complex dynamics than classical reservoirs, potentially leading to improved prediction accuracy. This work outlines the theoretical framework for QRC, detailing the setup of a suitable quantum reservoir (e.g., a superconducting qubit array), the measurement process, and the training strategy involving a classical machine learning model. We demonstrate a conceptual approach, focusing on the potential advantages and challenges of employing quantum systems for this task. The core claim is that QRC can achieve superior time series prediction accuracy compared to classical RC methods. Key elements explored include the input signal's influence on the quantum reservoir, the selection of appropriate measurement schemes, and the choice of classical learning algorithms. The paper concludes with a discussion of future research directions and the potential impact of QRC on various time series prediction applications.
This paper investigates the application of quantum reservoir computing (QRC) for adaptive control of complex dynamical systems. The core concept leverages quantum superposition and entanglement within a reservoir of coupled qubits to enhance the accuracy and speed of control signal generation. Unlike traditional reservoir computing, this approach explicitly utilizes quantum phenomena for improved performance. A small, randomly coupled quantum system, acting as the reservoir, is exposed to the dynamics of the system being controlled. The output of the reservoir is then used to train a classical controller, capitalizing on quantum interference effects. We present a theoretical framework outlining the methodology and demonstrate, through mathematical analysis, the potential advantages of QRC over conventional control methods, particularly for highly nonlinear systems. The work explores the key parameters influencing QRC performance, including the number of qubits, coupling strengths, and the training algorithm. Initial results suggest a significant improvement in control accuracy and convergence speed compared to classical reservoir approaches.
Reservoir computing is a promising neuromorphic paradigm, and its quantum implementation using spin networks has shown some advantage when entanglement is present. Here, we consider a distributed scenario in which two distinct input time series are injected into separate qubits of a spin-network reservoir. We investigate how the overall entanglement, as well as its localization in the system, influence the performance of the reservoir. Focusing on bilinear memory tasks that require computing the product of the two inputs, we evaluate the short-term memory capacity and correlate it with logarithmic negativity as a measure of bipartite entanglement. We find that short-term memory capacity reaches its maximum at relatively small coupling strengths. In contrast, average entanglement peaks at larger couplings. Analyzing entanglement across all bipartitions, we find that the entanglement between the two input qubits is consistently the strongest and most relevant for task performance. In the small coupling strength regime where the short-term memory capacity is maximized, the reservoir exhibits an extended memory tail: performance remains high for a long time. Finally, a pronounced dip in performance at zero time delay, observed across frequencies, indicates that information requires a finite propagation time through the reservoir before it can be effectively recalled. In summary, our results show that moderate entanglement, particularly between the two input qubits, plays a key role in enhancing short-term memory performance.