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Rozprawy doktorskie na temat „Quantum Machine Learning (QML)”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 25 lipca 2025

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1

Huembeli, Patrick. "Machine learning for quantum physics and quantum physics for machine learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672085.

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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and

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De, Bonis Gianluca. "Rassegna su Quantum Machine Learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24652/.

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Il Quantum Computing (QC) e il Machine Learning (ML) sono due dei settori più promettenti dell’informatica al giorno d’oggi. Il primo riguarda l’utilizzo di proprietà fisiche di sistemi quantistici per realizzare computazioni, mentre il secondo algoritmi di apprendimento automatizzati capaci di riconoscere pattern nei dati. In questo elaborato vengono esposti alcuni dei principali algoritmi di Quantum Machine Learning (QML), ovvero versioni quantistiche dei classici algoritmi di ML. Il tutto è strutturato come un’introduzione all’argomento: inizialmente viene introdotto il QC spiegandone le pr

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Du, Yuxuan. "The Power of Quantum Neural Networks in The Noisy Intermediate-Scale Quantum Era." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24976.

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Machine learning (ML) has revolutionized the world in recent years. Despite the success, the huge computational overhead required by ML models makes them approach the limits of Moore’s law. Quantum machine learning (QML) is a promising way to conquer this issue, empowered by Google's demonstration of quantum computational supremacy. Meanwhile, another cornerstone in QML is validating that quantum neural networks (QNNs) implemented on the noisy intermediate-scale quantum (NISQ) chips can accomplish classification and image generation tasks. Despite the experimental progress, little is known abo

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Macaluso, Antonio <1990&gt. "A Novel Framework for Quantum Machine Learning." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9791/2/Antonio_Macaluso_tesi.pdf.

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Quantum computation is an emerging computing paradigm with the potential to revolutionise the world of information technology. It leverages the laws of quantum mechanics to endow quantum machines with tremendous computing power, thus enabling the solution of problems impossible to address with classical devices. For this reason, the field is attracting ever-increasing attention from both academic and private sectors, and its full potential is still to be understood. This dissertation investigates how classical machine learning can benefit from quantum computing and provides several contributio

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Rodriguez, Fernandez Carlos Gustavo. "Machine learning quantum error correction codes : learning the toric code /." São Paulo, 2018. http://hdl.handle.net/11449/180319.

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Orientador: Mario Leandro Aolita<br>Banca:Alexandre Reily Rocha<br>Banca: Juan Felipe Carrasquilla<br>Resumo: Usamos métodos de aprendizagem supervisionada para estudar a decodiﬁcação de erros em códigos tóricos de diferentes tamanhos. Estudamos múltiplos modelos de erro, e obtemos ﬁguras da eﬁcácia de decodiﬁcação como uma função da taxa de erro de um único qubit. Também comentamos como o tamanho das redes neurais decodificadoras e seu tempo de treinamento aumentam com o tamanho do código tórico.<br>Abstract: We use supervised learning methods to study the error decoding in toric codes ofdiff

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TACCHINO, FRANCESCO. "Digital quantum simulations and machine learning on near-term quantum processors." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1317093.

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Quantum mechanics is the gateway towards novel and potentially disruptive approaches to scientific and technical computing. In this thesis we explore, from a number of different perspectives, the effects of such strong relationship between the physical nature of information and the informational side of physical processes, with a focus on the digital quantum computing paradigm. After an extensive introduction to the theory of universal quantum simulation techniques, we review the main achievements in the field and, in parallel, we outline the state of the art of near-term architectures for qu

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Lukac, Martin. "Quantum Inductive Learning and Quantum Logic Synthesis." PDXScholar, 2009. https://pdxscholar.library.pdx.edu/open_access_etds/2319.

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Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified aproach to QLS as Quantum Computing is still being discovered and novel applications are being identified. The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning. Based on algorithmic approach, I first design a Genetic Alg

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Cangini, Nicolò. "Quantum Supervised Learning: Algoritmi e implementazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17694/.

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Il Quantum Computing non riguarda più soltanto la scienza della Fisica, negli ultimi anni infatti la ricerca in questo campo ha subito una notevole espansione dimostrando l'enorme potenziale di cui dispongono questi nuovi calcolatori che in un futuro prossimo potranno rivoluzionare il concetto di Computer Science così come lo conosciamo. Ad oggi, siamo già in grado di realizzare algoritmi su piccola scala eseguibili in un quantum device grazie ai quali è possibile sperimentare uno speed-up notevole (in alcuni casi esponenziale) su diversi task tipici della computazione classica. In questo elab

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Orazi, Filippo. "Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25062/.

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Human society has always been shaped by its technology, so much that even ages and parts of our history are often named after the discoveries of that time. The growth of modern society is largely derived from the introduction of classical computers that brought us innovations like repeated tasks automatization and long-distance communication. However, this explosive technological advancement could be subjected to a heavy stop when computers reach physical limitations and the empirical law known as Moore Law comes to an end. Foreshadowing these limits and hoping for an even more powerful techno

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Felefly, Tony. "Quantum-classical machine learning for brain tumor imaging analysis." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAJ064.

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La caractérisation des tumeurs cérébrales par des techniques non invasives est nécéssaire. L'objectif est d'utiliser l'apprentissage automatique et la technologie quantique sur des imageries pour caractériser les tumeurs cérébrales. Nous développons un Réseau Neuronal Quantique en utilisant la radiomique des IRM cérébrales pour différencier métastases et gliomes de haut grade. Nous sélectionnons les variables en se basant sur l'information mutuelle et nous utilisons D-Wave pour la solution. Nous entraînons le modèle sur un Simulateur Quantique. Nous utilisons les valeurs de Shapley pour expliq

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Dalyac, Constantin. "Quantum many-body dynamics for combinatorial optimisation and machine learning." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS275.

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L'objectif de cette thèse est d'explorer et de qualifier l'utilisation de la dynamique quantique à N-corps pour résoudre des problèmes industriels difficiles et des tâches d'apprentissage automatique. En tant que collaboration entre des partenaires industriels et universitaires, cette thèse explore les capacités d'un dispositif à atome neutre pour résoudre des problèmes réels. Tout d'abord, nous examinons les problèmes d'optimisation combinatoire et montrons comment les atomes neutres peuvent naturellement encoder un célèbre problème d'optimisation combinatoire appelé le "Maximum Independent S

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Gupta, Riddhi Swaroop. "Robotic control and machine learning for the characterization and control of qubits." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23519.

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Decoherence remains a major challenge in Near term, Intermediate scale Noisy Quantum (NISQ) computers. This thesis proposes techniques for characterizing classical noise correlations and performance variations in NISQ devices using single-qubit projective measurements. The central tasks of qubit state estimation and prediction are considered for measurements that are either temporally or spatially correlated. Firstly, this thesis focuses on timeseries prediction for single-qubit measurement outcomes. Focusing on repeated Ramsey measurements performed on a single qubit subject to temporally cor

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Tingley, Michael Alan. "Towards the Quantum Machine: Using Scalable Machine Learning Methods to Predict Photovoltaic Efficacy of Organic Molecules." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:12553271.

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Recent advances in machine learning have resulted in an upsurge of interest in developing a “quantum machine”, a technique of simulating and predicting quantum-chemical properties on the molecular level. This paper explores the development of a large-scale quantum machine in the context of accurately and rapidly classifying molecules to determine photovoltaic efficacy through machine learning. Specifically, this paper proposes several novel representations of molecules that are amenable to learning, in addition to extending and improving existing representations. This paper also proposes and i

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14

Sriarunothai, Theeraphot [Verfasser]. "Multi-qubit gates and quantum-enhanced deliberation for machine learning using a trapped-ion quantum processor / Theeraphot Sriarunothai." Siegen : Universitätsbibliothek der Universität Siegen, 2019. http://d-nb.info/1177366320/34.

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Mills, Matthew. "A multipolar polarisable force field method from quantum chemical topology and machine learning." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/a-multipolar-polarisable-force-field-method-from-quantum-chemical-topology-and-machine-learning(3fb1e55c-0d4c-4d11-932b-71706bdbeb8b).html.

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Force field methods are used to investigate the properties of a wide variety of chemical systems on a routine basis. The expression for the electrostatic energy typically does not take into account the anisotropic nature of the atomic electron distribution or the dependence of that distribution on the system geometry. This has been suggested as a cause of the failure of force field methods to reliably predict the behaviour of chemical systems. A method for incorporation of anisotropy and polarisation is described in this work. Anisotropy is modelled by the inclusion of multipole moments centre

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Herzog, Basile. "Chemically accurate simulations by machine learning correlated approximations." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0126.

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Obtenir la précision chimique de façon systématique serait d'une aide considérable pour la découverte efficace de matériaux, la conception de médicaments ou l'étude de voies de réaction chimique. Alors que la théorie de la fonctionnelle de la densité est devenue la méthode de simulation de prédilection en science des matériaux, la qualité des résultats varie malheureusement souvent en fonction du choix spécifique de la fonctionnelle d'échange et corrélation, ce qui limite considérablement le pouvoir prédictif de cette méthode. Des approches plus corrélées, telles que la théorie du cluster coup

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Wu, Jiaxin. "Topics in Cold Atoms Related to Quantum Information Processing and A Machine Learning Approach to Condensed Matter Physics." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156320039156199.

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Zhang, Wei. "Many-Body Localization in Disordered Quantum Spin Chain and Finite-Temperature Gutzwiller Projection in Two-Dimensional Hubbard Model:." Thesis, Boston College, 2019. http://hdl.handle.net/2345/bc-ir:108695.

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Thesis advisor: Ziqiang . Wang<br>The transition between many-body localized states and the delocalized thermal states is an eigenstate phase transition at finite energy density outside the scope of conventional quantum statistical mechanics. We apply support vector machine (SVM) to study the phase transition between many-body localized and thermal phases in a disordered quantum Ising chain in a transverse external field. The many-body eigenstate energy E is bounded by a bandwidth W=Eₘₐₓ-Eₘᵢₙ. The transition takes place on a phase diagram spanned by the energy density ϵ=2(Eₘₐₓ-Eₘᵢₙ)/W and the

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Bauer, Carsten [Verfasser], Simon [Gutachter] Trebst, and Achim [Gutachter] Rosch. "Simulating and machine learning quantum criticality in a nearly antiferromagnetic metal / Carsten Bauer ; Gutachter: Simon Trebst, Achim Rosch." Köln : Universitäts- und Stadtbibliothek Köln, 2020. http://d-nb.info/1228071888/34.

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Li, Zhenwei. "On-the-fly machine learning of quantum mechanical forces and its potential applications for large scale molecular dynamics." Thesis, King's College London (University of London), 2014. http://kclpure.kcl.ac.uk/portal/en/theses/onthefly-machine-learning-of-quantum-mechanical-forces-and-its-potential-applications-for-large-scale-molecular-dynamics(2a2f33a6-fa0c-44e3-8689-f4cf3f1c9198).html.

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Material simulation using molecular dynamics (MD) at the quantum mechanical (QM) accuracy level has gained great interest in the community. However, the bottleneck arising from the O(N3) scaling of QM calculation has enormously limited its investigation scope. As an approach to address this issue, in this thesis, I proposed a machine-learning (ML) MD scheme based on Bayesian inference from CPU-intensive QM force database. In this scheme, QM calculations are only performed when necessary and used to augment the ML database for more challenging prediction case. The scheme is generally transferab

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Mori, Yuto. "Path optimization with neural network for sign problem in quantum field theories." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263466.

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Fyrillas, Andreas. "High-fidelity photonic quantum information : protocols and universal circuit control." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASP021.

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Dans cette thèse de doctorat, nous contribuons au domaine de la photonique quantique intégrée pour le traitement de l'information quantique, notamment via la caractérisation, le contrôle et les applications de circuits photoniques intégrés (PIC). Les PICs permettent une manipulation compacte et stable de la lumière quantique. Nous interfaçons des sources brillantes de photons uniques à base de boites quantiques semi-conductrices avec des PICs en silice ou en nitrure de silicium, afin d'implémenter des protocoles d'information quantique.Nous réalisons d'abord des protocoles quantiques à l'aide

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Fiderer, Lukas J. [Verfasser], and Daniel [Akademischer Betreuer] Braun. "New Concepts in Quantum Metrology : Dynamics, Machine Learning, and Bounds on Measurement Precision / Lukas J. Fiderer ; Betreuer: Daniel Braun." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/1212025334/34.

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Eisenhart, Andrew. "Quantum Simulations of Specific Ion Effects in Organic Solvents." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1626356392775228.

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Pérez, Salinas Adrián. "Algorithmic strategies for seizing quantum computing." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/673255.

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Quantum computing is an emergent technology with prospects to solve problems nowadays intractable. For this purpose it is a requirement to build computers capable to store and control quantum systems without losing their quantum properties. However, these computers are hard to achieve, and in the near term there will only be Noisy Intermediate-Scale Quantum (NISQ) computers with limited performance. In order to seize quantum computing during the NISQ era, algorithms with low resource demands and capable to return approximate solutions are explored. This thesis presents

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Bayo, Djénabou. "Détermination de phase par Deep Learning pour les systèmes désordonnés." Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1280.

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Ces dernières années, nous avons vu l'émergence d'un grand nombre de publications de machine learning (ML) dans les domaines de la physique de la matière condensée et de la physique statistique. En particulier, les outils de ML apparaissent comme des méthodes valides pour l'identification de phase. Dans cette thèse, nous étudions le ML sous le spectre de deux modèles. Le premier est le modèle de percolation de site en deux dimensions. Dans ce modèle paradigmatique, les sites sont occupés avec une probabilité p; une transition de phase du second ordre d'une phase non-percolante à une phase perc

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Yao, Yuan. "Automated design of photonic quantum circuits." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT005.

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L'informatique quantique est basée sur des phénomènes de physique quantique, tels que superposition et intrication et elle promet de révolutionner le monde de l'informatique. La photonique est une plateforme de premier plan pour réaliser l'informatique quantique tolérante aux erreurs. Elle possède plusieurs qualités : fonctionnement à température ambiante, fabricabilité à grande échelle à l'aide des fonderies existantes pour les puces de silicium et compatibilité avec les communications optique pour interconnecter différents ordinateurs quantiques.Notre objectif principal est d'automatiser la

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Maxwell, Peter. "FFLUX : towards a force field based on interacting quantum atoms and kriging." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/fflux-towards-a-force-field-based-on-interacting-quantum-atoms-and-kriging(72a8462a-6907-4f3d-82da-4c182e5a644d).html.

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Force fields have been an integral part of computational chemistry for decades, providing invaluable insight and facilitating the better understanding of biomolecular system behaviour. Despite the many benefits of a force field, there continue to be deficiencies as a result of the classical architecture they are based upon. Some deficiencies, such as a point charge electrostatic description instead of a multipole moment description, have been addressed over time, permitted by the ever-increasing computational power available. However, whilst incorporating such significant improvements has impr

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Österberg, Viktor. "Using Machine Learning to Develop a Quantum-Accurate Inter-Atomic Potential for Large Scale Molecular Dynamics Simulations of Iron under Earth’s Core Conditions." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298848.

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Measurements of iron at extreme pressures do not agree on the melting temperature for conditions comparable with those believed to hold at Earth's core. To attempt to determine the stability of relevant lattices, simulations involving a huge amount of particles are needed. In this thesis, a machine learned model is trained to yield results from density functional theory. Different machine learning models are compared. The trained model is then used in molecular dynamics simulations of relevant lattices at a scale too large for density functional theory.<br>Mätningar av järns smälttemperatur un

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Zauleck, Julius Philipp Paul [Verfasser], and Regina de [Akademischer Betreuer] Vivie-Riedle. "Improving grid based quantum dynamics : from the inclusion of solvents to the utilization of machine learning / Julius Philipp Paul Zauleck ; Betreuer: Regina de Vivie-Riedle." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1151818461/34.

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Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not hav

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Glasser, Ivan [Verfasser], Ignacio [Akademischer Betreuer] Cirac, Nora [Gutachter] Brambilla, and Ignacio [Gutachter] Cirac. "Tensor networks, conformal fields and machine learning: applications in the description of quantum many-body systems / Ivan Glasser ; Gutachter: Nora Brambilla, Ignacio Cirac ; Betreuer: Ignacio Cirac." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1173899057/34.

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Pronobis, Wiktor Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Alexandre [Gutachter] Tkatchenko, and Manfred [Gutachter] Opper. "Towards more efficient and performant computations in quantum chemistry with machine learning / Wiktor Pronobis ; Gutachter: Klaus-Robert Müller, Alexandre Tkatchenko, Manfred Opper ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1208764470/34.

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Pronobis, Wiktor [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Alexandre [Gutachter] Tkatchenko, and Manfred [Gutachter] Opper. "Towards more efficient and performant computations in quantum chemistry with machine learning / Wiktor Pronobis ; Gutachter: Klaus-Robert Müller, Alexandre Tkatchenko, Manfred Opper ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1208764470/34.

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August, Moritz [Verfasser], Thomas [Akademischer Betreuer] Huckle, José Miguel [Gutachter] Hernández-Lobato, Steffen J. [Gutachter] Glaser, and Thomas [Gutachter] Huckle. "Tensor networks and machine learning for approximating and optimizing functions in quantum physics / Moritz August ; Gutachter: José Miguel Hernández-Lobato, Steffen J. Glaser, Thomas Huckle ; Betreuer: Thomas Huckle." München : Universitätsbibliothek der TU München, 2018. http://d-nb.info/1175091804/34.

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Perea, Ospina Jose Dario [Verfasser], Salvador León [Akademischer Betreuer] Cabanillas, and Christoph J. [Gutachter] Brabec. "Solubility and Miscibility of Organic Semiconductors for Efficient and Stable Organic Solar Cells Investigated via Machine Learning and Quantum Chemistry Methods / Jose Dario Perea Ospina ; Gutachter: Christoph J. Brabec ; Betreuer: Salvador León Cabanillas." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2019. http://d-nb.info/1184575215/34.

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Théveniaut, Hugo. "Méthodes d'apprentissage automatique et phases quantiques de la matière." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30228.

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Mon travail de thèse s'est articulé autour de trois manières d'utiliser les méthodes d'apprentissage automatique (machine learning) en physique de la matière condensée. Premièrement, j'expliquerai comment il est possible de détecter automatiquement des transitions de phase en reformulant cette tâche comme un problème de classification d'images. J'ai testé la fiabilité et relevé les limites de cette approche dans des modèles présentant des phases localisées à N corps (many-body localized - MBL) en dimension 1 et en dimension 2. Deuxièmement, j'introduirai une représentation variationnelle d'éta

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Hoffmann, Guillaume. "Mise au point de nouveaux descripteurs théoriques pour la réactivité chimique Can molecular and atomic descriptors predict the electrophilicity of Michael acceptors? On the influence of dynamical effects on reactivity descriptors Predicting experimental electrophilicities from quantum and topological descriptors : a machine learning approach Electrophilicity indices and halogen bonds : some new alternatives to the molecular electrostatic potential." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR042.

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L’étude des descripteurs de réactivité globaux, locaux et non locaux d'un système réactif est d’une importance capitale afin de comprendre la réactivité de la totalité des processus chimiques lors d’une réaction. Le but de cette thèse a ainsi été de mettre au point de nouveaux descripteurs de réactivités, ainsi que des modèles de prédiction basés sur ces derniers, afin d’étudier la réactivité chimique. Les principale méthodes théoriques employées ont été la Théorie de la fonctionnelle de la densité conceptuelle (CDFT) et la théorie quantique « Atoms in Molecules » (QTAIM) qui sont toutes deux

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Leelar, Bhawani Shankar. "Machine Learning Algorithms Using Classical And Quantum Photonics." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4303.

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ABSTRACT In the modern day , we are witnessing two complementary trends, exponential growth in data and shrinking of chip size. The Data is approaching to 44 zettabytes by 2020 and the chips are now available with 10nm technology. The hyperconnectivity between machine-to-machine and humanto- machine creates multi-dimensional data which is more complex. Our thesis addresses the quantum meta layer abstraction which provides the interface to the Application layer to design quantum and classical algorithms. The first part of the thesis addresses the quantum algorithms and second part address cl

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Pipicelli, Claudio. "Quantum Machine Learning: A Comparison Between Quantum and Classical Support Vector Machine." Tesi di dottorato, 2020. http://www.fedoa.unina.it/13259/1/PhDThesisPipicelliFinal.pdf.

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This thesis is mainly focused on the study of Quantum Support Vector Machine (QSVM), a very important member of the recent and innovative Quantum Machine Learning field, and its comparison with conventional Support Vector Machine (SVM). In this paper, I have worked on the application of Quantum Support Vector Machine algorithm, that runs on near term quantum processors from I.B.M., through IBM Quantum Experience cloud service, to a set of supervised machine learning case studies and I compared its performance with classical Support Vector Machine algorithm; net of the enormous hype surrounding

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Tranter, Aaron. "Machine learning for quantum and complex systems." Phd thesis, 2021. http://hdl.handle.net/1885/220395.

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Machine learning now plays a pivotal role in our society, providing solutions to problems that were previously thought intractable. The meteoric rise of this technology can no doubt be attributed to the information age that we now live in. As data is continually amassed, more efficient and scalable methods are required to yield functional models and accurate inferences. Simultaneously we have also seen quantum technology come to the forefront of research and next generation systems. These technologies promise secure information transfer, efficient computation and high precision sensing, at

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Buffoni, Lorenzo. "Machine learning applications in science." Doctoral thesis, 2021. http://hdl.handle.net/2158/1227616.

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Machine learning is a broad field of study, with multifaceted applications of cross-disciplinary breadth that ultimately aims at developing computer algorithms that improve automatically through experience. The core idea of artificial intelligence technology is that systems can learn from data, so as to identify distinctive patterns and make consequently decisions, with minimal human intervention. The range of applications of these methodologies is already extremely vast, and still growing at a steady pace due to the pressing need to cope with the efficiently handling of big data. In parallel

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Huang, Yufeng. "Computational Heterogeneous Electrochemistry – From Quantum Mechanics to Machine Learning." Thesis, 2019. https://thesis.library.caltech.edu/11328/13/Yufeng_thesis_2019.02.11.pdf.

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<p>Because of coulomb interactions and complex surface morphologies, rigorous methods for heterogeneous electrochemical catalysis were not well-established. Thus, for different types of electrochemical systems, a specific strategy must be adapted. In this thesis, we first used the cluster model to study the chemistry on a 1D chain of MoS<sub>2</sub> edges. Then, a rigorous grand canonical potential kinetics (GCP-K) method was developed for general crystalline systems. Starting from quantum mechanical calculations, the method gave rise to a different picture from the traditional description giv

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"Machine learning for optical communications, nonlinear optics, and quantum optics." Tulane University, 2020.

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"Classical and quantum data sketching with applications in communication complexity and machine learning." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291567.

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Liu, Yang.<br>Thesis Ph.D. Chinese University of Hong Kong 2014.<br>Includes bibliographical references (leaves 163-188).<br>Abstracts also in Chinese.<br>Title from PDF title page (viewed on 25, October, 2016).

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"Control of classical & quantum multispatial modes of light for quantum networks through nonlinear optics and machine learning." Tulane University, 2020.

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archives@tulane.edu<br>With the advent of lasers the spatial shape of paraxial light also became an avenue for information processing and transfer applications. The light sources that support multiple of these spatial modes as separate, multiplexed information channels are readily used through classical optical implementations such as free-space optical communication, and to enhance the capacity of these channels. Recently, the hot atomic vapour based non-linear optical systems showed promise for the usage of paraxial multiple spatial modes of light for quantum information applications such as

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Mengoni, Riccardo. "Quantum Approaches to Data Science and Data Analytics." Doctoral thesis, 2020. http://hdl.handle.net/11562/1018231.

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In this thesis are explored different research directions related to both the use of classical data analysis techniques for the study of quantum systems and the employment of quantum computing to speed up hard Machine Learning tasks

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Fu, chien wei, and 傅建維. "Using quantum chemistry, molecular simulation and machine learning techniques to study the enzymatic mechanism for several enzymes." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/j556k8.

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博士<br>國立清華大學<br>分子醫學研究所<br>103<br>Chapter I: Site of metabolism prediction for FMO enzymes via machine learning and condensed Fukui function The flavin-containing monooxygenase (FMO) catalyzes xenobiotics with soft nucleophiles and also plays an important role in drug metabolism in Phase I enzymes. The site of metabolism (SOM) refers to the place where the reaction of metabolism occurs in a molecule. Identification of SOMs of a compound is not usually a low-cost task in drug discovery. Thus, a silico method to predict site of metabolism (SOMs) of FMOs would provide medical chemists information

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Slatyer, Harry James. "Multi-parameter optimisation of quantum optical systems." Phd thesis, 2018. http://hdl.handle.net/1885/146120.

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Quantum optical systems are poised to become integral components of technologies of the future. While there is growing commercial interest in these systems---for applications in information processing, secure communication and precision metrology---there remain significant technical challenges to overcome before widespread adoption is possible. In this thesis we consider the general problem of optimising quantum optical systems, with a focus on sensing and information processing applications. We investigate four different classes of system with varying d

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Swann, Ellen Therese. "Development and application of statistical and quantum mechanical methods for modelling molecular ensembles." Phd thesis, 2018. http://hdl.handle.net/1885/142784.

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The development of new quantum chemical methods requires extensive benchmarking to establish the accuracy and limitations of a method. Current benchmarking practices in computational chemistry use test sets that are subject to human biases and as such can be fundamentally flawed. This work presents a thorough benchmark of diffusion Monte Carlo (DMC) for a range of systems and properties as well as a novel method for developing new, unbiased test sets using multivariate statistical techniques. Firstly, the hydrogen abstraction of methanol is used as

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