This thesis presents original contributions at the intersection of quantum computing and machine learning, investigating how these two fields can benefit from each other. The first part of the thesis presents two applications of machine learning techniques in quantum computing. The first contribution involves the use of artificial neural networks for the decoding of quantum error correction codes, resulting in faster decoding times while maintaining a high level of accuracy. The originality of this work lies in the application of explainable machine learning techniques to both improve the decoding performance and to provide insights into the decoding process. The second contribution introduces a novel technique based on reinforcement learning to characterize and simulate the noise affecting a quantum chip. This technique reduces the heuristic assumption on the noise model, making it more adaptable to the specific noise characteristics of the quantum device. In the second part of the thesis, we demonstrate, with two examples, how it is possible to implement machine learning algorithms on quantum devices by replacing artificial neural networks with parametrized quantum circuits. The first contribution in this section introduces a quantum anomaly detection algorithm, applied to the field of high-energy physics. This algorithm allows for the identification of anomalous patterns in quantum data, maintaining a level of accuracy comparable to classical algorithms. The novelty of this work lies in the first use of quantum circuits for the task of anomaly detection in a muon drift chamber trigger system, with possible future applications to quantum detector systems in high-energy physics experiments. The second contribution presents the quantum version of a generative diffusion model, able to sample quantum states from a well-defined distribution. This first implementation of a quantum diffusion model is not meant to outperform classical models, but rather to show potential advantages and limitations of these kinds of algorithms. The aim of the thesis is to show how quantum computing can benefit from machine learning, and how machine learning can be implemented on quantum devices, setting the stage for future advancements in both fields.
This thesis presents original contributions at the intersection of quantum computing and machine learning, investigating how these two fields can benefit from each other. The first part of the thesis presents two applications of machine learning techniques in quantum computing. The first contribution involves the use of artificial neural networks for the decoding of quantum error correction codes, resulting in faster decoding times while maintaining a high level of accuracy. The originality of this work lies in the application of explainable machine learning techniques to both improve the decoding performance and to provide insights into the decoding process. The second contribution introduces a novel technique based on reinforcement learning to characterize and simulate the noise affecting a quantum chip. This technique reduces the heuristic assumption on the noise model, making it more adaptable to the specific noise characteristics of the quantum device. In the second part of the thesis, we demonstrate, with two examples, how it is possible to implement machine learning algorithms on quantum devices by replacing artificial neural networks with parametrized quantum circuits. The first contribution in this section introduces a quantum anomaly detection algorithm, applied to the field of high-energy physics. This algorithm allows for the identification of anomalous patterns in quantum data, maintaining a level of accuracy comparable to classical algorithms. The novelty of this work lies in the first use of quantum circuits for the task of anomaly detection in a muon drift chamber trigger system, with possible future applications to quantum detector systems in high-energy physics experiments. The second contribution presents the quantum version of a generative diffusion model, able to sample quantum states from a well-defined distribution. This first implementation of a quantum diffusion model is not meant to outperform classical models, but rather to show potential advantages and limitations of these kinds of algorithms. The aim of the thesis is to show how quantum computing can benefit from machine learning, and how machine learning can be implemented on quantum devices, setting the stage for future advancements in both fields.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
This thesis investigates the interplay between machine learning (ML) and quantum mechanics to advance quantum machine learning (QML) and quantum calculations, addressing computational challenges in quantum machine learning, materials science, and condensed matter physics. The high dimensionality of quantum systems often renders traditional methods computationally intensive. By integrating classical and quantum ML techniques, innovative algorithms are developed to enhance efficiency and accuracy. The first part investigates the design and optimization of quantum circuit architectures for quantum machine learning, with a focus on quantum support vector machines (QSVM). I develop a Bayesian algorithm that adaptively constructs quantum kernels, achieving superior performance over classical kernels in small-data classification tasks. I also establish an isomorphism between quantum circuits and polyatomic molecules, which enables the restriction of the search space for compositional optimization of quantum circuits. Additionally, I introduce a flexible scheme that maps quantum circuits into graph representations compatible with the neutral atom quantum hardware. Using this circuit-graph mapping scheme, I develop a Gaussian process regression model with quantum evolution kernels and a classical graph neural network to interpolate QSVM accuracy on graph-structured data. The second part applies ML to accelerate quantum calculations. I develop a variational algorithm based on eigenvector continuation to compute ground state energies of particle-phonon systems. This algorithm constructs the lowest eigenvalue and eigenvector for extended lattices by solving eigenvalue problems for small, independent lattice segments, with a lower computational cost. This enables the application of variational quantum eigensolvers to particle-phonon interactions in large and disordered lattices, reducing the demands on quantum resources. I also developed Gaussian process models to accelerate Green’s function calculations for electron-phonon systems, achieving high accuracy by extrapolating from restricted Hilbert spaces to larger systems with lower computational cost. Ultimately, this thesis demonstrates that these advancements pave the way for applications, including the design of materials, enhanced drug discovery, and the development of scalable quantum computing technologies, thereby positioning the integration of ML and quantum physics as a promising approach for future scientific breakthroughs.
This thesis investigates the interplay between machine learning (ML) and quantum mechanics to advance quantum machine learning (QML) and quantum calculations, addressing computational challenges in quantum machine learning, materials science, and condensed matter physics. The high dimensionality of quantum systems often renders traditional methods computationally intensive. By integrating classical and quantum ML techniques, innovative algorithms are developed to enhance efficiency and accuracy. The first part investigates the design and optimization of quantum circuit architectures for quantum machine learning, with a focus on quantum support vector machines (QSVM). I develop a Bayesian algorithm that adaptively constructs quantum kernels, achieving superior performance over classical kernels in small-data classification tasks. I also establish an isomorphism between quantum circuits and polyatomic molecules, which enables the restriction of the search space for compositional optimization of quantum circuits. Additionally, I introduce a flexible scheme that maps quantum circuits into graph representations compatible with the neutral atom quantum hardware. Using this circuit-graph mapping scheme, I develop a Gaussian process regression model with quantum evolution kernels and a classical graph neural network to interpolate QSVM accuracy on graph-structured data. The second part applies ML to accelerate quantum calculations. I develop a variational algorithm based on eigenvector continuation to compute ground state energies of particle-phonon systems. This algorithm constructs the lowest eigenvalue and eigenvector for extended lattices by solving eigenvalue problems for small, independent lattice segments, with a lower computational cost. This enables the application of variational quantum eigensolvers to particle-phonon interactions in large and disordered lattices, reducing the demands on quantum resources. I also developed Gaussian process models to accelerate Green’s function calculations for electron-phonon systems, achieving high accuracy by extrapolating from restricted Hilbert spaces to larger systems with lower computational cost. Ultimately, this thesis demonstrates that these advancements pave the way for applications, including the design of materials, enhanced drug discovery, and the development of scalable quantum computing technologies, thereby positioning the integration of ML and quantum physics as a promising approach for future scientific breakthroughs.
This Ph.D. thesis provides a comprehensive review of the state-of-the-art in the field of Variational Quantum Algorithms and Quantum Machine Learning, including numerous original contributions. The first chapters are devoted to a brief summary of quantum computing and an in-depth analysis of variational quantum algorithms. The discussion then shifts to quantum machine learning, where an introduction to the elements of machine learning and statistical learning theory is followed by a review of the most common quantum counterparts of machine learning models. Next, several novel contributions to the field based on previous work are presented, namely: a newly introduced model for a quantum perceptron with applications to recognition and classification tasks; a variational generalization of such a model to reduce the circuit footprint of the proposed architecture; an industrial use case of a quantum autoencoder followed by a quantum classifier used to analyze classical data from an industrial power plant; a study of the entanglement features of quantum neural network circuits; and finally, a noise deconvolution technique to remove a large class of noise when performing arbitrary measurements on qubit systems.
At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades. With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.
The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical resources, generative quantum machine learning can also employ quantum resources - such as parameterized quantum channels and quantum operators - to learn and sample from the probability model of interest. Applications of generative (quantum) models are multifaceted. The trained model can generate new samples that are compatible with the given data and extend the data set. Additionally, learning a model for the generation process of a data set may provide interesting information about the corresponding properties. With the help of quantum resources, the respective generative models have access to functions that are difficult to evaluate with a classical computer and may improve the performance or lead to new insights. Furthermore, generative quantum machine learning can be applied to efficient, approximate loading of classical data into a quantum state which may help to avoid - potentially exponentially - expensive, exact quantum data loading. The aim of this doctoral thesis is to develop new generative quantum machine learning algorithms, demonstrate their feasibility, and analyze their performance. Additionally, we outline their potential application to efficient, approximate quantum data loading. More specifically, we introduce a quantum generative adversarial network and a quantum Boltzmann machine implementation, both of which can be realized with parameterized quantum circuits. These algorithms are compatible with first-generation quantum hardware and, thus, enable us to study proof of concept implementations not only with numerical quantum simulations but also real quantum hardware available today.