Quantum computation is a form of computation utilizing quantum mechanical phenomena.A formal formulation of quantum computer was proposed in 1985, leading to novel quantum algorithms whose computation time scales better, with respect to the problem size, than the classical counterparts.However, running these quantum algorithms requires fault-tolerant implementations, and hence, such engineering in quantum systems can be an overhead in utilizing quantum systems for information processing.This suggests, in addition to seeking faster quantum algorithms, the necessity of exploring how to harness quantum systems more naturally without extensive engineering, leading to another form of quantum information processing.Quantum machine learning (QML) is expected to be a promising approach for harnessing quantum systems for information processing.This is motivated by the fact that the success of neural networks has led to the possibility of solving ML tasks more efficiently with non-digital computer designs.Several QML algorithms that resemble neural networks utilize quantum systems in a manner distinct from digital quantum algorithms.However, training quantum systems is fundamentally challenging, suggesting that it still remains necessary to explore an alternative way of harnessing quantum systems for ML.Along with this direction, the quantum reservoir model harnesses quantum systems in a unique way: nontrivial outputs from an untrained quantum system are leveraged by a linear classical machine learning model to solve various tasks.Hence, the quantum part does not need to be programmable and can be analog quantum dynamics.Without extensive engineering in the quantum part, a quantum reservoir model was shown to be capable of performing nontrivial ML tasks using physical dynamics, which had already been realized experimentally.Therefore, the quantum reservoir model should be one of the promising directions for pursuing another form of quantum information processing.In this thesis, I will investigate the computational capability of natural quantum dynamics in the quantum reservoir model.I will tackle the following critical open questions in this context: (i) how well natural dynamics perform nontrivial ML tasks compared to engineered systems, and (ii) what kind of engineering in quantum dynamics is essential and appropriate for a given task.To tackle the first question (i), I will benchmark the learning performance of various natural quantum dynamics against random circuits that require complicated engineering, thereby revealing the computational power of natural dynamics.For the second question (ii), I will present a characterization method of quantum dynamics that has the potential to bridge quantum properties and their machine learning roles, providing concrete insights into essential and appropriate engineering to design quantum systems for ML.v
Quantum computation is a form of computation utilizing quantum mechanical phenomena.A formal formulation of quantum computer was proposed in 1985, leading to novel quantum algorithms whose computation time scales better, with respect to the problem size, than the classical counterparts.However, running these quantum algorithms requires fault-tolerant implementations, and hence, such engineering in quantum systems can be an overhead in utilizing quantum systems for information processing.This suggests, in addition to seeking faster quantum algorithms, the necessity of exploring how to harness quantum systems more naturally without extensive engineering, leading to another form of quantum information processing.Quantum machine learning (QML) is expected to be a promising approach for harnessing quantum systems for information processing.This is motivated by the fact that the success of neural networks has led to the possibility of solving ML tasks more efficiently with non-digital computer designs.Several QML algorithms that resemble neural networks utilize quantum systems in a manner distinct from digital quantum algorithms.However, training quantum systems is fundamentally challenging, suggesting that it still remains necessary to explore an alternative way of harnessing quantum systems for ML.Along with this direction, the quantum reservoir model harnesses quantum systems in a unique way: nontrivial outputs from an untrained quantum system are leveraged by a linear classical machine learning model to solve various tasks.Hence, the quantum part does not need to be programmable and can be analog quantum dynamics.Without extensive engineering in the quantum part, a quantum reservoir model was shown to be capable of performing nontrivial ML tasks using physical dynamics, which had already been realized experimentally.Therefore, the quantum reservoir model should be one of the promising directions for pursuing another form of quantum information processing.In this thesis, I will investigate the computational capability of natural quantum dynamics in the quantum reservoir model.I will tackle the following critical open questions in this context: (i) how well natural dynamics perform nontrivial ML tasks compared to engineered systems, and (ii) what kind of engineering in quantum dynamics is essential and appropriate for a given task.To tackle the first question (i), I will benchmark the learning performance of various natural quantum dynamics against random circuits that require complicated engineering, thereby revealing the computational power of natural dynamics.For the second question (ii), I will present a characterization method of quantum dynamics that has the potential to bridge quantum properties and their machine learning roles, providing concrete insights into essential and appropriate engineering to design quantum systems for ML.v
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
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.
Quantum machine learning (QML) has gained significant interest by combining quantum computing and artificial intelligence, two topics that are expected to revolutionize the way of data processing. While gate-based quantum computing uses precisely defined unitary operations on qubits, currently available noisy intermediate-scale quantum (NISQ) devices are not ready for implementations of high-depth circuits. However, machine learning is one of the promising applications to be used on NISQ hardware via parameterized quantum circuits (PQCs). An alternative QML paradigm comes from a different direction: Quantum reservoir computing (QRC) relies on using physical systems as quantum artificial neural networks. Instead of using controlled gate operations, here the system dynamics are controlled by the underlying Hamiltonian, and the machine learning layer is performed at the output weights. Despite their distinct origin, both ML approaches are connected and can formally be mapped onto each other. We discuss this analogy by realizing a transverse-field Ising model on a gate-based quantum computing architecture establishing a fundamental connection on an abstract level between these two very different QML paradigms. This enables us to connect the known expressivity measure of PQC-QML with the quantification of expressiveness of physical dynamical systems.
Machine Learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum Machine Learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics