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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 we are on the verge of adopting some quantum technologies in our daily life. Quantum many-body physics is a subfield of quantum physics where we study the collective behavior of particles or atoms and the emergence of phenomena that are due to this collective behavior, such as phases of matter. The study of phase transitions of these systems often requires some intuition of how we can quantify the order parameter of a phase. ML algorithms can imitate something similar to intuition by inferring knowledge from example data. They can, therefore, discover patterns that are invisible to the human eye, which makes them excellent candidates to study phase transitions. At the same time, quantum devices are known to be able to perform some computational task exponentially faster than classical computers and they are able to produce data patterns that are hard to simulate on classical computers. Therefore, there is the hope that ML algorithms run on quantum devices show an advantage over their classical analog.
This thesis is devoted to study two different paths along the front lines of ML and quantum physics. On one side, we study the use of neural networks (NN) to classify phases of mater in many-body quantum systems. On the other side, we study ML algorithms that run on quantum computers. The connection between ML for quantum physics and quantum physics for ML in this thesis is an emerging subfield in ML, the interpretability of learning algorithms. A crucial ingredient in the study of phase transitions with NNs is a better understanding of the predictions of the NN, to eventually infer a model of the quantum system and interpretability can assist us in this endeavor. The interpretability method that we study analyzes the influence of the training points on a test prediction and it depends on the curvature of the NN loss landscape. This further inspired an in-depth study of the loss of quantum machine learning (QML) applications which we as well will discuss.
In this thesis, we give answers to the questions of how we can leverage NNs to classify phases of matter and we use a method that allows to do domain adaptation to transfer the learned "intuition" from systems without noise onto systems with noise. To map the phase diagram of quantum many-body systems in a fully unsupervised manner, we study a method known from anomaly detection that allows us to reduce the human input to a mini mum. We will as well use interpretability methods to study NNs that are trained to distinguish phases of matter to understand if the NNs are learning something similar to an order parameter and if their way of learning can be made more accessible to humans. And finally, inspired by the interpretability of classical NNs, we develop tools to study the loss landscapes of variational quantum circuits to identify possible differences between classical and quantum ML algorithms that might be leveraged for a quantum advantage.La investigación en la intersección del aprendizaje automático (machine learning, ML) y la física cuántica es una área en crecimiento reciente debido al éxito y las enormes expectativas de ambas áreas. ML es posiblemente una de las tecnologías más prometedoras que ha alterado y seguirá alterando muchos aspectos de nuestras vidas. Es casi seguro que la forma en que investigamos no es una excepción y el ML, con su capacidad sin precedentes para encontrar patrones ocultos en los datos ayudará a futuros descubrimientos científicos. La física cuántica, por otro lado, aunque a veces no es del todo intuitiva, es una de las teorías físicas más exitosas, y además estamos a punto de adoptar algunas tecnologías cuánticas en nuestra vida diaria. La física cuántica de los muchos cuerpos (many-body) es una subárea de la física cuántica donde estudiamos el comportamiento colectivo de partículas o átomos y la aparición de fenómenos que se deben a este comportamiento colectivo, como las fases de la materia. El estudio de las transiciones de fase de estos sistemas a menudo requiere cierta intuición de cómo podemos cuantificar el parámetro de orden de una fase. Los algoritmos de ML pueden imitar algo similar a la intuición al inferir conocimientos a partir de datos de ejemplo. Por lo tanto, pueden descubrir patrones que son invisibles para el ojo humano, lo que los convierte en excelentes candidatos para estudiar las transiciones de fase. Al mismo tiempo, se sabe que los dispositivos cuánticos pueden realizar algunas tareas computacionales exponencialmente más rápido que los ordenadores clásicos y pueden producir patrones de datos que son difíciles de simular en los ordenadores clásicos. Por lo tanto, existe la esperanza de que los algoritmos ML que se ejecutan en dispositivos cuánticos muestren una ventaja sobre su analógico clásico. Estudiamos dos caminos diferentes a lo largo de la vanguardia del ML y la física cuántica. Por un lado, estudiamos el uso de redes neuronales (neural network, NN) para clasificar las fases de la materia en sistemas cuánticos de muchos cuerpos. Por otro lado, estudiamos los algoritmos ML que se ejecutan en ordenadores cuánticos. La conexión entre ML para la física cuántica y la física cuántica para ML en esta tesis es un subárea emergente en ML: la interpretabilidad de los algoritmos de aprendizaje. Un ingrediente crucial en el estudio de las transiciones de fase con NN es una mejor comprensión de las predicciones de la NN, para inferir un modelo del sistema cuántico. Así pues, la interpretabilidad de la NN puede ayudarnos en este esfuerzo. El estudio de la interpretabilitad inspiró además un estudio en profundidad de la pérdida de aplicaciones de aprendizaje automático cuántico (quantum machine learning, QML) que también discutiremos. En esta tesis damos respuesta a las preguntas de cómo podemos aprovechar las NN para clasificar las fases de la materia y utilizamos un método que permite hacer una adaptación de dominio para transferir la "intuición" aprendida de sistemas sin ruido a sistemas con ruido. Para mapear el diagrama de fase de los sistemas cuánticos de muchos cuerpos de una manera totalmente no supervisada, estudiamos un método conocido de detección de anomalías que nos permite reducir la entrada humana al mínimo. También usaremos métodos de interpretabilidad para estudiar las NN que están entrenadas para distinguir fases de la materia para comprender si las NN están aprendiendo algo similar a un parámetro de orden y si su forma de aprendizaje puede ser más accesible para los humanos. Y finalmente, inspirados por la interpretabilidad de las NN clásicas, desarrollamos herramientas para estudiar los paisajes de pérdida de los circuitos cuánticos variacionales para identificar posibles diferencias entre los algoritmos ML clásicos y cuánticos que podrían aprovecharse para obtener una ventaja cuántica.
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Müller, Markus. "Quantum Kolmogorov complexity and the quantum turing machine." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1655.
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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 proprietà più rilevanti, successivamente vengono descritti gli algoritmi di QML confrontandoli con le loro controparti classiche e infine vengono discusse le principali tecnologie attuali, mostrando alcune implementazioni degli algoritmi precedentemente discussi.
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Vicente, Nieto Irene. "Towards Machine Translation with Quantum Computers." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196602.
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This work explores the possibilities to use quantum computers and quantum based language models for machine translation. Precise translation requires vast expertise and knowledge of various languages, thus machine translationis still far from superseding humans. Quantum computers could improve machine translation due to their high computational power, as they benefit from properties such as superposition and entanglement to process data faster and in parallel. We focused our work on the DIStributional COmpositional CATegorical (DisCoCat) semantics and its python toolbox DisCoPy developed by [1]. We built and transformed simple, complex, and negative English and Spanish sentences to DisCoCat diagrams. Those diagrams are then used as input to quantum circuits, allowing us to perform calculations in NISQ devices providedby IBMQ. The calculations show that a quantum computer can understand the meaning of simple and complex sentences in different languages, and this is the first step to perform translation with Quantum Computers. In addition, we worked on preserving sentence meaning by measuring the cosine similarity between two vectorised sentences and obtained sentence similarities scores of 95%.
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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 about the theoretical advances of QNNs. In this thesis, we explore the power of QNNs to fill this knowledge gap.
First, we consider the potential advantages of QNNs in generative learning. We demonstrate that QNNs possess a stronger expressive power than that of classical neural networks in the measure of computational complexity and entanglement entropy. Moreover, we employ QNNs to tackle synthetic generation tasks with state-of-the-art performance.
Next, we propose a Grover-search based quantum classifier, which can tackle specific classification tasks with quadratic runtime speedups. Furthermore, we exhibit that the proposed scheme allows batch gradient descent optimization, which is different from previous studies. This property is crucial to train large-scale datasets.
Then, we study the capabilities and limitations of QNNs in the view of optimization theory and learning theory. The achieved results imply that a large system noise can destroy the trainability of QNNs. Meanwhile, we show that QNNs can tackle parity learning and juntas learning with provable advantages.
Last, we devise a quantum auto-ML scheme to enhance the trainability QNNs under the NISQ setting. The achieved results indicate that our proposal effectively mitigates system noise and alleviates barren plateaus for both conventional machine learning and quantum chemistry tasks.
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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 quantum information processing. We then move on to present novel and scalable procedures for the study of paradigmatic spin models on intermediate-scale noisy quantum processors. Through an innovative combination of quantum algorithms with classical post-processing and error mitigation protocols, we demonstrate in practice the full digital quantum simulation of spin-spin dynamical correlation functions, reporting experimental results obtained on superconducting cloud-accessible IBM Q devices. We also exhibit a practical use-case by successfully reproducing, from quantum computed data, cross section calculations for four-dimensional inelastic neutron scattering, a common tool employed in the analysis of molecular magnetic clusters.
The central part of the thesis is dedicated to the exploration of perspective hardware solutions for quantum computing. As it is not yet clear whether the currently dominant platforms, namely trapped ions and superconducting circuits, will eventually allow to reach the final goal of a fully-fledged architecture for general-purpose quantum information processing, the search for alternative technologies is at least as urgent as the improvement of existing ones or the development of new algorithms. After providing an overview of some recent proposals, including hybrid set-ups, we introduce quantum electromechanics as a promising candidate platform for future realizations of digital quantum simulators and we predict competitive performances for an elementary building block featuring nanomechanical qubits integrated within superconducting circuitry.
In the final part, we extend the reach of quantum information protocols beyond its traditional areas of application, and we account for the birth and rapid development of Quantum Machine Learning, a discipline aimed at establishing a productive interplay between the parallel revolutions brought about by quantum computing and artificial intelligence. In particular, we describe an original procedure for implementing, on a quantum architecture, the behavior of binary-valued artificial neurons. Formally exact and platform-independent, our data encoding and processing scheme guarantees in principle an exponential memory advantage over classical counterparts and is particularly well suited for pattern and image recognition purposes. We test our algorithm on IBM Q quantum processors, discussing possible training schemes for single nodes and reporting a proof-of-principle demonstration of a 2-layer, 3-neuron feed-forward neural network computation run on 7 active qubits. The latter is, in terms of the total size of the quantum register, one of the largest quantum neural network computation reported to date on real quantum hardware.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 quantum information processing. We then move on to present novel and scalable procedures for the study of paradigmatic spin models on intermediate-scale noisy quantum processors. Through an innovative combination of quantum algorithms with classical post-processing and error mitigation protocols, we demonstrate in practice the full digital quantum simulation of spin-spin dynamical correlation functions, reporting experimental results obtained on superconducting cloud-accessible IBM Q devices. We also exhibit a practical use-case by successfully reproducing, from quantum computed data, cross section calculations for four-dimensional inelastic neutron scattering, a common tool employed in the analysis of molecular magnetic clusters. The central part of the thesis is dedicated to the exploration of perspective hardware solutions for quantum computing. As it is not yet clear whether the currently dominant platforms, namely trapped ions and superconducting circuits, will eventually allow to reach the final goal of a fully-fledged architecture for general-purpose quantum information processing, the search for alternative technologies is at least as urgent as the improvement of existing ones or the development of new algorithms. After providing an overview of some recent proposals, including hybrid set-ups, we introduce quantum electromechanics as a promising candidate platform for future realizations of digital quantum simulators and we predict competitive performances for an elementary building block featuring nanomechanical qubits integrated within superconducting circuitry. In the final part, we extend the reach of quantum information protocols beyond its traditional areas of application, and we account for the birth and rapid development of Quantum Machine Learning, a discipline aimed at establishing a productive interplay between the parallel revolutions brought about by quantum computing and artificial intelligence. In particular, we describe an original procedure for implementing, on a quantum architecture, the behavior of binary-valued artificial neurons. Formally exact and platform-independent, our data encoding and processing scheme guarantees in principle an exponential memory advantage over classical counterparts and is particularly well suited for pattern and image recognition purposes. We test our algorithm on IBM Q quantum processors, discussing possible training schemes for single nodes and reporting a proof-of-principle demonstration of a 2-layer, 3-neuron feed-forward neural network computation run on 7 active qubits. The latter is, in terms of the total size of the quantum register, one of the largest quantum neural network computation reported to date on real quantum hardware.
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Sjöstrand, Joachim. "Engineering superconducting qubits : towards a quantum machine." Doctoral thesis, Stockholm University, Department of Physics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-818.
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A quantum computer is an information processing machine, much like an ordinary classical computer, but its function is based on quantum mechanical principles. To be able to construct such a machine would be a fantastic accomplishment---to have total control over a quantum system is a dream for both physicists and science-fiction enthusiasts. The basic information unit in a quantum computer is the quantum bit, or qubit for short. A quantum computer consists of many coupled qubits. To get a single qubit to work properly, would be a major step towards building this machine.
Here we study two different qubit ideas. The central element in both setups is the superconducting tunnel junction---the Josephson junction. By connecting the Josephson junctions to standard electronics in a clever way, a qubit can be realised. With these constructions it is in principle very easy to manipulate and read out the quantum probabilities, by varying voltages and currents in time. However, this ease of manipulation has a cost: strong interactions with uncontrolled degrees of freedom of the environment transfer information from the qubit. For superconducting qubits this decoherence is typically very fast.
There are ways to deal with the decoherence. One way is to tune the circuit parameters so that the decoherence becomes minimal. Another way is to engineer the qubits so fast so that the effect of decoherence becomes small. In this thesis, we will apply both these strategies. Specifically, the measurement speed of the second qubit we study, turns out to be very sensitive to the topology of the phase space of the detector variables.
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Sjöstrand, Joachim. "Engineering superconducting qubits : towards a quantum machine /." Stockholm : Department of Physics, Stockholm University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-818.
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Macaluso, Antonio <1990>. "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 contributions to the emerging field of Quantum Machine Learning.
The idea is to provide a universal and efficient framework that can reproduce the output of a plethora of classical machine learning algorithms exploiting quantum computation’s advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple functions to solve typical supervised learning tasks. Thanks to this property, in its general formulation MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions. The theoretical design of the quantum algorithm and the corresponding circuit’s implementation are presented. As a second meaningful addition, two practical applications are illustrated: the quantum version of ensemble methods and neural networks.
The final contribution addresses the restriction to linear operations imposed by quantum mechanics. The idea is to exploit a quantum transposition of classical Splines to approximate non-linear functions, thus overcoming this limitation and introducing significant advantages in terms of computational complexity theory.
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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 technology, forty years ago the branch of quantum computation was born. Quantum computation uses at its advantage the same quantum effects that could stop the progress of traditional computation and aim to deliver hardware and software capable of even greater computational power. In this context, this thesis presents the implementation of a quantum variational machine learning algorithm called quantum single-layer perceptron. We start by briefly explaining the foundation of quantum computing and machine learning, to later dive into the theoretical approach of the multiple aggregator quantum algorithms, and finally deliver a versatile implementation of the quantum counterparts of a single hidden layer perceptron. To conclude we train the model to perform binary classification using standard benchmark datasets, alongside three baseline quantum machine learning models taken from the literature. We then perform tests on both simulated quantum hardware and real devices to compare the performances of the various models.
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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 Algorithm for Quantum Logic Synthesis that is used to prove and verify the methods proposed in this work.
Based on results obtained from the evolutionary experimentation, I propose a fast, structure and cost based exhaustive search that is used for the design of a novel, least expensive universal family of quantum gates.
The results form both the evolutionary and heuristic search are used to formulate an Inductive Learning Approach based on Quantum Logic Synthesis with the intended application being the humanoid behavioral robotics.
The presented approach illustrates a successful algorithmic approach, where the search algorithm was able to invent/discover novel quantum circuits as well as novel principles in Quantum Logic Synthesis.
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Colledan, Andrea. "Abstract Machine Semantics for Quipper." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22835/.
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Quipper is a domain-specific programming language for the description of quantum circuits. Because it is implemented as an embedded language in Haskell, Quipper is a very practical functional language. However, for the same reason, it lacks a formal semantics and it is limited by Haskell's type-system. In particular, because Haskell lacks linear types, it is easy to write Quipper programs that violate the non-cloning property of quantum states. In order to formalize relevant fragments of Quipper in a type-safe way, the Proto-Quipper family of research languages has been introduced over the last years. In this thesis we first introduce Quipper and Proto-Quipper-M. Proto-Quipper-M is an instance of the Proto-Quipper family based on a categorical model for quantum circuits, which features a linear type-system that guarantees that the non-cloning property holds at compile time. We then derive a tentative small-step operational semantics from the big-step semantics of Proto-Quipper-M and we prove that the two are equivalent. After proving subject reduction and progress results for the tentative semantics, we build upon it to obtain a truly small-step semantics in the style of an abstract machine, which we eventually prove to be equivalent to the original semantics.
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ERDMAN, Paolo Andrea. "Quantum Thermal Machines." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/95512.
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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 elaborato vengono discusse le basi del Quantum Computing, con un focus particolare sulla possibilità di eseguire alcuni algoritmi supervisionati di Machine Learning in un quantum device per ottenere uno speed-up sostanziale nella fase di training. Oltre che una impostazione teorica del problema, vengono effettuati diversi esperimenti utilizzando le funzionalità dell'ambiente Qiskit, grazie al quale è possibile sia simulare il comportamento di un computer quantistico in un calcolatore classico, sia eseguirlo in cloud sui computer messi a disposizione da IBM.
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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 correlated dephasing, the key challenge is to predict qubit state dynamics by learning temporal noise correlations in a real-time stream of incoming measurements. Autoregressive or Fourier-based Kalman Filtering (KF) protocols are investigated for maximizing the forward prediction horizon and autoregressive approaches demonstrate superior predictive capabilities. Secondly, this thesis investigates the utility of nonlinear stochastic methods to characterize quantum systems using spatially correlated single-qubit projective measurements, via particle filters. A novel likelihood function is presented and incorporated into conventional particle filters. An adaptive procedure, `NMQA', is proposed to characterize a spatially inhomogeneous dephasing field in 2D by allocating measurements on a multi-qubit array. NMQA outperforms brute-force mapping in simulations and using experimental data. Finally, spatial prediction in 2D is compared with bivariate interpolation on a geometric arrangement of points known as the Padua points. By measuring a spatially inhomogeneous dephasing field on dedicated `sensor qubits', the objective is to predict the value of the field on unmeasured, proximal `data qubits'. The number and arrangement of sensor qubits relative to a fixed lattice of data qubits is investigated, providing insights for a general purpose predictive mapping protocol.
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Streed, Erik William. "⁸⁷Rubidium Bose-Einstein condensates : machine construction and quantum Zeno experiments." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34400.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2006.Includes bibliographical references (p. 121-130).
This thesis details construction of a new apparatus for the production of 87Rb Bose-Einstein condensates and a subsequent quantum Zeno effect experiment. An experimental apparatus for producing large Bose-Einstein condensates of 87Rb is described in detail. A high flux thermal atomic beam is decelerated by a Zeeman slower and is then captured and cooled in a magneto-optical trap. The atoms are then transfered into a cloverleaf style Ioffe-Pritchard magnetic trap and cooled to quantum degeneracy with radio frequency induced forced evaporation. Condensates containing up to 20 million atoms can be produced every few minutes. The quantum Zeno effect is the suppression of transitions between quantum states by frequent measurement. Oscillation between two ground hyperfine states of a magnetically trapped 87Rb Bose-Einstein condensate, externally driven at a transition rate WR, was substantially suppressed by destructively measuring one of the levels with resonant optical scattering. While an ideal continuous measurement will stop the transition, any real measurement method will occur at a finite rate. The suppression of the transition rate in the two level system was quantified for pulsed measurements with a time between pulses t and weak continuous measurements with a scattering rate y. We observe that the weak continuous measurements exhibit the same suppression in the transition rate as the pulsed measurements when ySt = 3.60(0.43). This is in agreement with the previously predicted value of 4. Increasing the measurement frequency suppressed the transition rate to 0.005WR.
by Erik William Streed.
Ph.D.
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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 AolitaBanca:Alexandre Reily Rocha
Banca: Juan Felipe Carrasquilla
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.
Abstract: We use supervised learning methods to study the error decoding in toric codes ofdifferent sizes. We study multiple error models, and obtain figures of the decoding efficacyas a function of the single qubit error rate. We also comment on how the size of thedecoding neural networks and their training time scales with the size of the toric code
Mestre
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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 implements extensions to scalable distributed learning algorithms, in order to perform large scale molecular regression. This paper leverages Harvard’s Odyssey supercomputer in order to train various kinds of predictive algorithms over millions of molecules, and assesses cross-validated test performance of these models for predicting photovoltaic efficacy. The study suggests combinations of representations and learning models that may be most desirable in constructing a large-scale system designed to classify molecules by photovoltaic efficacy.
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Harrysson, Patrik. "Memory Cost of Quantum Contextuality." Thesis, Linköpings universitet, Informationskodning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131007.
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This is a study taking an information theoretic approach toward quantum contextuality. The approach is that of using the memory complexity of finite-state machines to quantify quantum contextuality. These machines simulate the outcome behaviour of sequential measurements on systems of quantum bits as predicted by quantum mechanics. Of interest is the question of whether or not classical representations by finite-state machines are able to effectively represent the state-independent contextual outcome behaviour. Here we consider spatial efficiency, rather than temporal efficiency as considered by D. Gottesman (1999), for the particular measurement dynamics in systems of quantum bits. Extensions of cases found in the adjacent study of Kleinmann et al. (2010) are established by which upper bounds on memory complexity for particular scenarios are found. Furthermore, an optimal machine structure for simulating any n-partite system of quantum bits is found, by which a lower bound for the memory complexity is found for each n in the natural numbers. Within this finite-state machine approach questions of foundational concerns on quantum mechanics were sought to be addressed. Alas, nothing of novel thought on such concerns is here reported on.
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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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Rossini, Davide. "Quantum information processing and Quantum spin systems." Doctoral thesis, Scuola Normale Superiore, 2007. http://hdl.handle.net/11384/85856.
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Mazza, Francesco. "Multi-terminal thermoeletric machines in nanoscale systems." Doctoral thesis, Scuola Normale Superiore, 2016. http://hdl.handle.net/11384/86204.
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From the introduction: "The objective of this thesis is to fill the gap in the knowledge of thermoelectric properties of multi-terminal systems. Therefore we started by studying the multi-terminal problem in its full complexity, with no constraints on the leads. We soon realized that the freedom given by three-terminal systems could be used to develop a scheme to spatially separate the heat and charge flows, obtaining a significant increase in both the power and the efficiency delivered by the engine. By adding the additional complication of a magnetic field on three-terminal systems, we were able to design a thermal magnetic switch which provides a simple way to deal with heat management at the nanoscale".
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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 centred at atoms whose values are determined by application of the methods of Quantum Chemical Topology. Polarisation, the dependence of the electron distribution on system geometry, is modelled by training machine learning models to predict atomic multipole moments from knowledge of the nuclear positions of a system. The resulting electrostatic method can be implemented for any chemical system. An application to progressively more complex systems is reported, including small organic molecules and larger molecules of biological importance. The accuracy of the method is rigorously assessed by comparison of its predictions to exact interaction energy values. A procedure for generating transferable atomic multipole moment models is defined and tested. The electrostatic method can be combined with the empirical expressions used in force field calculations to describe total system energies by fitting parameters against ab initio conformational energies. Derivatives of the energy are given and the resulting multipolar polarisable force field can be used to perform geometry optimisation calculations. Future applications to conformational searching and problems requiring dynamic descriptions of a system are feasible.
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Pang, Bo. "Handwriting Chinese character recognition based on quantum particle swarm optimization support vector machine." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950620.
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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 two different algorithmic strategies aiming to contribute to the plethora of algorithms available for NISQ devices, namely re-uploading and strategy. Each strategy takes advantage of different features of quantum computing, namely the superposition and the density of the Hilbert space in re-uploading, and entanglement among different partitions of the system in unary, to overcome a variety of obstacles. In both cases, the strategies are general and can be applied in a range of scenarios. Some examples are also provided in this thesis.
First, the re-uploading is designed as a meeting point between quantum computing and machine learning. Machine learning is a set of techniques to build computer programs capable to learn how to solve a problem through experience, without being explicitly programmed for it. Even though the re-uploading is not the first attempt to join quantum computers and machine learning, this approach has certain properties that make it different from other methods.
In particular, the re-uploading approach consists in introducing data into a classical algorithms in different stages along the process. This is a main difference with respect to standard methods, where data is uploaded at the beginning of the procedure. In the re-uploading, data is accompanied by tunable classical parameters that are optimized by a classical method according to a cost function defining the problem. The joint action of data and tunable parameters grant the quantum algorithm a great flexibility to learn a given behavior from sampling target data. The more re- uploadings are used, the better results can be obtained.
In this thesis, re-uploading is presented by means of a set of theoretical results supporting its capabilities, and simulations and experiments to benchmark its performance in a variety of problems.
The second algorithmic strategy is unary. This strategy describes a problem making use of only a small part of the available computational space. Thus, the computational capabilites of the computer are not optimal. In exchange, the operations required to execute a certain task become simpler. As a consequence, the retrieved results are more resilient to noise and decoherence, and meaningful. Therefore, a trade-off between efficiency and resillience against noise arises. NISQ computers benefit from this circumstance, especially in the case of small problems, where even quantum advantage and advantage over standard algorithms can be achieved.
In this thesis, unary is used to solve a typical problem in finance called option pricing, which is of interest for real world applications. Options are contracts to buy the right to buy/sell a given asset at certain time and price. The holder of the option will only exercise this right in case of profit. Option pricing concists in estimating this profit by handling stochastic evolution models.
This thesis aims to contribute to the growing number of algorithms available for NISQ computers and pave the way towards new quantum technologies.La computación cuántica es una tecnología emergente con potencial para resolver problemas hoy impracticables. Para ello son necesarios ordenadores capaces de mantener sistemas cuánticos y controlarlos con precisión. Sin embargo, construir estos ordenadores es complejo y a corto plazo solo habrá ordenadores pequeños afectados por el ruido y sujetos a ruido (NISQ). Para aprovechar los ordenadores NISQ se exploran algoritmos que requieran pocos recursos cuánticos mientras proporcionan soluciones aproximadas a los problemas que enfrentan. En esta tesis se estudian dos propuestas para algoritmos NISQ: re-uploading y unary. Cada estrategia busca tomar ventaja de diferentes características de la computación cuántica para superar diferentes obstáculos. Ambas estrategias son generales y aplicables en diversos escenarios. En primer lugar, re-uploading está diseñado como un puente entre la computación cuántica y el aprendizaje automático (Machine Learning). Aunque no es el primer intento de aplicar la cuántica al aprendizaje automático, re-uploading tiene ciertas características que lo distinguen de otros métodos. En concreto, re-uploading consiste en introducir datos en un algoritmo cuántico en diferentes puntos a lo largo del proceso. Junto a los datos se utilizan también parámetros optimizables clásicamente que permiten al circuito aprender cualquier comportamiento. Los resultados mejoran cuantas más veces se introducen los datos. El re-uploading cuenta con teoremas matemáticos que sustentan sus capacidades, y ha sido comprobado con éxito en diferentes situaciones tanto simuladas como experimentales. La segunda estrategia algorítmica es unary. Consiste en describir los problemas utilizando solo parte del espacio de computación disponible dentro del ordenador. Así, las capacidades computacionales del ordenador no son óptimas, pero a cambio las operaciones necesarias para una cierta tarea se simplifican. Los resultados obtenidos son resistentes al ruido, y mantienen su significado, y se produce una compensación entre eficiencia y resistencia a errores. Los ordenadores NISQ se ven beneficiados de esta situación para problemas pequeños. En esta tesis, unary se utiliza para resolver un problema tíıpico de finanzas, incluso obteniendo ventajas cuánticas en un problema aplicable al mundo real. Con esta tesis se espera contribuir al crecimiento de los algoritmos disponibles para ordenadores cuánticos NISQ y allanar el camino para las tecnologías venideras.
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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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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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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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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 . WangThe 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 disorder strength ẟJ of the spin interaction uniformly distributed within [-ẟJ, ẟJ], formally parallel to the mobility edge in Anderson localization. In our study we use the labeled probability density of eigenstate wavefunctions belonging to the deeply localized and thermal regimes at two different energy densities (ϵ's) as the training set, i.e., providing labeled data at four corners of the phase diagram. Then we employ the trained SVM to predict the whole phase diagram. The obtained phase boundary qualitatively agrees with previous work using entanglement entropy to characterize these two phases. We further analyze the decision function of the SVM to interpret its physical meaning and find that it is analogous to the inverse participation ratio in configuration space. Our findings demonstrate the ability of the SVM to capture potential quantities that may characterize the many-body localization phase transition. To further investigate the properties of the transition, we study the behavior of the entanglement entropy of a subsystem of size L_A in a system of size L > L_A near the critical regime of the many-body localization transition. The many-body eigenstates are obtained by exact diagonalization of a disordered quantum spin chain under twisted boundary conditions to reduce the finite-size effect. We present a scaling theory based on the assumption that the transition is continuous and use the subsystem size L_A/ξ as the scaling variable, where ξ is the correlation length. We show that this scaling theory provides an effective description of the critical behavior and that the entanglement entropy follows the thermal volume law at the transition point. We extract the critical exponent governing the divergence of ξ upon approaching the transition point. We again study the participation entropy in the spin-basis of the domain wall excitations and show that the transition point and the critical exponent agree with those obtained from finite size scaling of the entanglement entropy. Our findings suggest that the many-body localization transition in this model is continuous and describable as a localization transition in the many-body configuration space. Besides the many-body localization transition driven by disorder, We also study the Coulomb repulsion and temperature driving phase transitions. We apply a finite-temperature Gutzwiller projection to two-dimensional Hubbard model by constructing a "Gutzwiller-type" density matrix operator to approximate the real interacting density matrix, which provides the upper bound of free energy of the system. We firstly investigate half filled Hubbard model without magnetism and obtain the phase diagram. The transition line is of first order at finite temperature, ending at 2 second order points, which shares qualitative agreement with dynamic mean field results. We derive the analytic form of the free energy and therefor the equation of states, which benefits the understanding of the different phases. We later extend our approach to take anti-ferromagnetic order into account. We determine the Neel temperature and explore its interesting behavior when varying the Coulomb repulsion
Thesis (PhD) — Boston College, 2019
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Physics
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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 transferable to new chemical situations and database completeness is never required. To achieve the maximal ML eciency, I use a symmetrically reduced internal-vector representation for the atomic congurations. Signicant speed-up factor is achieved under controllable accuracy tolerance in the MD simulation on test case of Silicon at dierent temperatures. As the database grows in conguration space, the extrapolative capability systematically increases and QM calculations are nally not needed for simple chemical processes. In the on-the-y ML force calculation scheme, sorting/selecting out the closest data congurations is used to enhance the overall eciency to scale as O(N). The potential application of this methodology for large-scale simulation (e.g. fracture, amorphous, defect), where chemical accuracy and computational eciency are required at the same time, can be anticipated. In the context of fracture simulations, a typical multi-scale system, interesting events happen near the crack tips beyond the description of classical potentials. The simulation results by machine-learning potential derived from a xed database with no enforced QM accuracy inspire a theoretical model which is further used to investigate the atomic bond breaking process during fracture propagation as well as its relation with the initialised vibration modes, crack speed, and bonding structure.
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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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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 improved force field accuracy, many still fail to describe several chemical effects including polarisation, non-covalent interactions and secondary/tertiary structural effects. Furthermore, force fields often fail to provide consistency when compared with other force fields. In other words, no force field is reliably performing more accurately than others, when applied to a variety of related problems. The work presented herein develops a next-generation force field entitled FFLUX, which features a novel architecture very different to any other force field. FFLUX is designed to capture the relationship between geometry and energy through a machine learning method known as kriging. Instead of a series of parameterised potentials, FFLUX uses a collection of atomic energy kriging models to make energy predictions. The energies describing atoms within FFLUX are obtained from the Interacting Quantum Atoms (IQA) energy partitioning approach, which in turn derives the energies from the electron density and nuclear charges of topological atoms described by Quantum Chemical Topology (QCT). IQA energies are shown to provide a unique insight into the relationship between geometry and energy, allowing the identification of explicit atoms and energies contributing towards torsional barriers within various systems. The IQA energies can be modelled to within 2.6% accuracy, as shown for a series of small systems including weakly bound complexes. The energies also allow an interpretation of how an atom feels its surrounding environment through intra-atomic, covalent and electrostatic energetic descriptions, which typically are seen to converge within a ~7 - 8 A horizon radius around an atom or small system. These energy convergence results are particularly relevant to tackling the transferability theme within force field development. Where energies are seen to converge, a proximity limit on the geometrical description needed for a transferable energy model is defined. Finally, the FFLUX force field is validated through successfully optimising distorted geometries of a series of small molecules, to near-ab initio accuracy.
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Barbarino, Simone. "Interaction effects in one-dimensional helical liquids." Doctoral thesis, Scuola Normale Superiore, 2016. http://hdl.handle.net/11384/86201.
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Helical liquids are one-dimensional quantum many-body systems where the low-energy excitations have spin and momentum degrees of freedom locked together. They can be realized in several different setups such as semiconducting wires, carbon nanotubes, and ultracold atoms trapped in optical lattices. They also appear as edge states of two-dimensional topological insulators. As a result of spin-momentum locking, they exhibit peculiar transport properties and represent promising platforms for the realization of Majorana fermions when coupled to superconductors; moreover, in the presence of particle-particle interactions, they can enter a fractional helical phase whose low-energy excitations have a fractional charge and a fractional Abelian statistics. In this thesis we study various aspects of interacting one-dimensional helical liquids. Firstly, we consider a one-dimensional chain of alkaline earth-(like) atoms hosting low-energy helical excitations and, by means of the synthetic dimension framework, we show that it is equivalent to an interacting fermionic ladder pierced by a constant magnetic field which displays features related to the quantum Hall effect. In these synthetic ladders, we investigate how helical excitations analogous to the chiral edge modes of the quantum Hall effect are affected by repulsive interactions and we find the existence of a hierarchy of fully gapped phases characterized by peculiar charge and spin orders. Then, we study the charge and the spin patterns of a quantum dot embedded into a spin-orbit coupled quantum wire subject to a magnetic field and we explore how the 2π-periodic and the 4π-periodic Josephson currents in a superconductor-helical liquidsuperconductor hybrid junction are affected by interactions. Finally, we show that, by entangling two identical particles with a qubit, we can transmute the quantum statistics of particles in a scattering experiment which could be realized using the helical edge states of a two-dimensional topological insulator.
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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 having explicit symmetries should exhibit to promote the quantum walk. Simulations will only answer the question for one single case, and will not provide any general rules for properties the graph should have. Using artificial neural networks (ANNs) as an aid for detecting when the quantum walk is faster on average than random walk on graphs, going from an initial node to a target node, has been done before. The quantum speedup may not be more than polynomial if the initial state of the quantum walk is purely in the initial node of the graph. We investigate starting the quantum walk in various superposition states, with an additional auxiliary node, to maybe achieve a larger quantum speedup. We suggest different ways to add the auxiliary node and select one of these schemes for use in this thesis. The superposition states examined are two stabiliser states and two magic states, inspired by the Gottesman-Knill theorem. According to this theorem, starting a quantum algorithm in a magic state may give an exponential speedup, but starting in a stabilizer state cannot give an exponential speedup, given that only gates from the Clifford group are used in the algorithm, as well as measurements are performed in the Pauli basis. We show that it is possible to train an ANN to classify graphs into what quantum walk was the fastest for various initial states of the quantum walk. The ANN classifies linear graphs and random graphs better than a random guess. We also show that a convolutional neural network (CNN) with a deeper architecture than earlier proposed for the task, is better at classifying the graphs than before. Our findings pave the way for automated research in novel quantum walk-based algorithms.Slumpvandringar på grafer är essensiella i viktiga algoritmer för att lösa olika problem, till exempel SAT, booleska uppfyllningsproblem (the satisfiability problem). Genom att göra slumpvandringar snabbare går det att förbättra dessa algoritmer. Kvantversionen av slumpvandringar, kvantvandringar, har visats vara snabbare än klassiska slumpvandringar i specifika fall, till exempel på vissa linjära grafer. Det går att analysera, analytiskt eller genom att simulera vandringarna på grafer, när kvantvandringen är snabbare än slumpvandingen. Problem uppstår dock när graferna blir större, har fler noder samt fler kanter. Det finns inga kända generella regler för vad en godtycklig graf, som inte har några explicita symmetrier, borde uppfylla för att främja kvantvandringen. Simuleringar kommer bara besvara frågan för ett enda fall. De kommer inte att ge några generella regler för vilka egenskaper grafer borde ha. Artificiella neuronnät (ANN) har tidigare används som hjälpmedel för att upptäcka när kvantvandringen är snabbare än slumpvandingen på grafer. Då jämförs tiden det tar i genomsnitt att ta sig från startnoden till slutnoden. Dock är det inte säkert att få kvantacceleration för vandringen om initialtillståndet för kvantvandringen är helt i startnoden. I det här projektet undersöker vi om det går att få en större kvantacceleration hos kvantvandringen genom att starta den i superposition med en extra nod. Vi föreslår olika sätt att lägga till den extra noden till grafen och sen väljer vi en för att använda i resen av projektet. De superpositionstillstånd som undersöks är två av stabilisatortillstånden och två magiska tillstång. Valen av dessa tillstånd är inspirerat av Gottesmann- Knill satsen. Enligt satsen så kan en algoritm som startar i ett magiskt tillstånd ha en exponetiell uppsnabbning, men att starta i någon stabilisatortillstånden inte kan ha det. Detta givet att grindarna som används i algoritmen är från Cliffordgruppen samt att alla mätningar är i Paulibasen. I projektet visar vi att det är möjligt att träna en ANN så att den kan klassificera grafer utifrån vilken kvantvandring, med olika initialtillstånd, som var snabbast. Artificiella neuronnätet kan klassificera linjära grafer och slumpmässiga grafer bättre än slumpen. Vi visar också att faltningsnätverk med en djupare arkitektur än tidigare föreslaget för uppgiften är bättre på att klassificera grafer än innan. Våra resultat banar vägen för en automatiserad forskning i nya kvantvandringsbaserade algoritmer.
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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.Mätningar av järns smälttemperatur under påfrestningar jämförbara med desom tros gälla i jordens kärna överensstämmer ej. För att försöka bestämma stabiliteten av relevanta gitter krävs simulationer av enorma mängder partiklar. I denna tes tränas en maskininlärd modell att återge resultat från Täthetsfunktionalteori. Olika maskininlärningsmodeller jämförs. Den tränade modellen används sedan i Molekyldynamik-simulationer av relevanta gitter som är förstora för Täthetsfunktionalteori.
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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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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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Frennessen, Sebastian. "A comparison of peak trunk rotational power and club head speed in elite golf players." Thesis, Högskolan i Halmstad, Bio- och miljösystemforskning (BLESS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-31039.
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Abstract Background: Golf is a sport with a growing focus on the physical aspect of the game and its relationship to performance. Studies have determined a correlation between club head speed and performance in golf. Rotational power has proven to be an important factor for the club head speed. By examining the relationship between club head speed and rotational power, researchers has found that rotation power on the golfers dominant side have a moderate to high correlation with club head speed. Previous research has mostly investigated the peak rotational power on the dominant side. Furthermore, additional research is needed to examine the bilateral strength and its relationship to club head speed. Aim: The aim of this study was to examine the correlation between peak trunk rotational power and club head speed in elite golfers, and also to study the impact of bilateral rotational strength on club head speed. Methods: The study included 27 elite golf players (21 males, 6 females) age 19±2 years. The subjects attended two sessions where the first session included a club head speed test and the second session a rotation power test in the Quantum machine. The rotational peak power ratio (dominant/non-dominant side) were ranged from 1-27 (the closer to 1, the higher order) to study a linier relationship with club head speed. Spearman’s nonparametric rank correlations coefficient (rs) was used since the data was not normally distributed. Results: There was a moderate correlation between peak trunk rotational power on the dominant side and club head speed ( rs=0.58, p=0.01). The correlation between the peak trunk rotational powers on the dominant and non- dominant side was high, rs=0.82 (p=0.01). There were no significant correlation found between the ranged rotational peak power ratio and club head speed (rs=0.30, p=0.1). Conclusion: The current study found a slightly lower correlation between peak trunk rotational power and club head speed than found in earlier studies. The golfers in this study had symmetric strength in the trunk, other studies have shown that the rotational strength in golfer´s dominant side were higher than of the non- dominant side. The result of this study indicates that balance between the sides not necessarily has a relationship with how high the golfer’s club head speed is. Future research is needed to analyze the quadratic correlation between ratio and club head speed on a more advanced level. The results of this study can, if validated, be used for further researching and understanding of club head speed and golf performance.
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Grilo, Alex Bredariol 1987. "Computação quântica e teoria de computação." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275508.
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Orientador: Arnaldo Vieira MouraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
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Resumo: A Computação Quântica é um tópico relativamente recente e pouco conhecido, principalmente no meio da Computação. Seu estudo surgiu na tentativa de físicos simularem sistemas regidos pela Mecânica Quântica por computadores clássicos, o que se conjecturou inviável. Portanto, um novo modelo computacional que utiliza a estrutura quântica da matéria para computar foi teorizado para suprir estas deficiências. Este trabalho tem como objetivo principal estudar as influências da Computação Quântica na Teoria da Computação. Para atingir tal objetivo, primeiramente são expostos os conhecimentos básicos da Mecânica Quântica através de uma linguagem voltada para Teóricos de Computação sem conhecimento prévio na área, de forma a remover a barreira inicial sobre o tema. Em seguida, serão apresentadas inovações na área da Teoria de Computação oriundas da Computação Quântica. Começaremos com os principais Algoritmos Quânticos desenvolvidos até hoje, que foram os primeiros passos para demonstrar a possível superioridade computacional do novo modelo. Dentre estes algoritmos, apresentaremos o famoso Algoritmo de Shor, que fatora números em tempo polinomial. Adicionalmente, neste trabalho foram estudados tópicos mais avançados e atuais em Computabilidade e Complexidade Quânticas. Sobre Autômatos Quânticos, foram estudados aspectos de um modelo que mistura estados clássicos e quânticos, focando na comparação do poder computacional em relação aos Autômatos Finitos Clássicos. Do ponto de vista de Classes de Complexidade, será abordada a questão se em linguagens da classe QMA, o análogo quântico da classe NP, consegue-se atingir probabilidade de erro nulo na aceitação de instâncias positivas
Abstract: Quantum Computing is a relatively new area and it is not well known, mainly among Computer Scientists. It has emerged while physicists tried to simulate Quantum Systems with classical computers efficiently, which has been conjectured impossible. Then, a new computational model that uses the quantum structure of matter to perform computations has been theorized in order to perform these operations. We intend in this work to study the influences of Quantum Computing in Theoretical Computer Science. In order to achieve this goal, we start by presenting the basics of Quantum Computing to Theoretical Computer Science readers with no previous knowledge in this area, removing any initial barriers for a clean understanding of the topic. We will then follow by showing innovations in Theoretical Computer Science introduced by Quantum Computation. We start by showing the main Quantum Algorithms, that exemplify advantages of the new computational model. Among these algorithms, we will present the Shor Algorithm that factors numbers in polynomial time. We follow with more advanced topics in Quantum Computability and Complexity. We study Quantum Finite Automata Models that work with quantum and classical states, focusing on comparing their computational power with Deterministic Finite Automata. In Complexity Theory, we study the question if for languages in QMA, the quantum analogue of NP, zero probability error can be achieved in yes-instances
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
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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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Cramer, Claes Richard. "Quantum aspects of time-machines." Thesis, University of York, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265661.
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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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Frenzel, Max. "Autonomous machines and clocks in quantum thermodynamics." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45286.
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Thermodynamics is one of the core disciplines of physics, and despite its long history it is still a very active area of research. Particularly in the regime of non-equilibrium processes and at small scales many open questions remain. In this thesis we critically study the role that autonomous machines play in the emerging field of quantum thermodynamics. We show that autonomous machines generally comprise several interacting subsystems that can take on various roles. In Part I we look at the issue of external control. Any non-autonomous machine by definition requires some external agent to interact with the machine. This agent is generally assumed to be classical and to not suffer any back-actions from the machine. If all the involved systems are of microscopic size this approximation is no longer valid and we have to include all the control mechanisms within a unified quantum framework. We show that in order to avoid external control and operate autonomously, a machine that is to extract work from another system requires an inbuilt quantum clock. Being itself a finite-size quantum system, such a clock will inevitably experience back-actions and develop correlations with the other systems, which lead to its degradation as a time keeping device. We show that this degradation can be counteracted by a judicious choice of quantum measurements. These measurements not only allow us to stabilise the clock, but also magnify thermodynamic properties such as work from the quantum scale, where their definition can be ambiguous, to the well understood classical scale. In Part II we consider the scenario of an autonomous quantum machine interacting with a thermal environment, for which we are experimentally restricted to only observe a subset of all the possible environment interactions, while the remaining ones are hidden from direct observation. Using a modification of the notion of quantum jump trajectories we show that the visible interactions in many cases still allow us to make some inferences about the hidden interactions. We introduce a new quantity, the coarse-grained hidden entropy, which quantifies the entropy production in the hidden subsystem conditioned on our observations of the visible part. The total entropy production consisting of the sum of visible and coarse-grained hidden entropy is shown to satisfy an integral fluctuations theorem. Depending on the information flow between the subsystems, the hidden entropy can assume negative values in which case the hidden systems acts as a Maxwell's demon. This behaviour is also captured by a modified second law like inequality which gives a refinement of the conventional second law for autonomous quantum machines with continuous information flows between the machine's subsystems.
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Malabarba, Artur S. L. "Equilibration and thermal machines in quantum mechanics." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.686239.
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This dissertation is about two aspects of quantum thermodynamics, quantum equilibration and thermal machines. First, we investigate equilibration of quantum systems with regards to typical measurements. Considering any Hamiltonian, any initial state, and measurements with a small number of outcomes compared to the dimension, we show that most measurements are already equilibrated. When the initial state is an eigenstate of the observable, most observables are initially out of equilibrium yet equilibrate more rapidly than would be physically reasonable. In search for more physical equilibration times, we turn to two practical scenario: a quantum particle in a one-dimensional box, as observed by a coarse grained position measurement; and a subsystem interacting with a highly mixed environment. We show that equilibration in both of these contexts indeed takes place and does so in very reasonable time scales. Back to a more general context, we present a theory independent definition of equilibration, and show that equilibration of pure states is objectively easier for quantum systems than for classical systems. This shows that quantum equilibration is a fundamental aspect of physical systems, while classical equilibration relies on experimental ignorance. In the subject of thermal machines, we show that a quantum system (the clock) can be used to exactly implement any energy-conserving unitary operation on an engine. When the engine includes a quantum work storage device we can approximately perform completely general unitaries. This can be used to carry out arbitrary transformations of a system without external control. We then show that autonomous thermal machines suffer no intrinsic thermodynamic cost compared to externally controlled ones. Finally, we further improve this construction by showing that the results still hold if the clock and the work storage device are given a more physical Hamiltonian.
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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'états fondamentaux sous la forme de réseaux de neurones (neural-network quantum states - NQS). Je présenterai nos résultats sur un modèle contraint de bosons de coeur dur en deux dimensions avec des méthodes variationnelles basées sur des NQS et de projection guidée. Nos travaux montrent notamment que les états NQS peuvent encoder avec précision des états solides et liquides de bosons. Enfin, je présenterai une nouvelle approche pour la recherche de stratégies de corrections d'erreur dans les codes quantiques, cette approche se base sur les techniques utilisées pour concevoir l'intelligence artificielle AlphaGo. Nous avons pu montrer que des stratégies efficaces peuvent être découvertes avec des algorithmes d'optimisation évolutionnistes. En particulier, nous avons observé que des réseaux de neurones peu profonds sont compétitifs avec les réseaux profonds utilisés dans des travaux antérieurs, représentant un gain d'un facteur 10000 en termes de nombre de paramètresMy PhD thesis presents three applications of machine learning to condensed matter theory. Firstly, I will explain how the problem of detecting phase transitions can be rephrased as an image classification task, paving the way to the automatic mapping of phase diagrams. I tested the reliability of this approach and showed its limits for models exhibiting a many-body localized phase in 1 and 2 dimensions. Secondly, I will introduce a variational representation of quantum many-body ground-states in the form of neural-networks and show our results on a constrained model of hardcore bosons in 2d using variational and projection methods. In particular, we confirmed the phase diagram obtained independently earlier and extends its validity to larger system sizes. Moreover we also established the ability of neural-network quantum states to approximate accurately solid and liquid bosonic phases of matter. Finally, I will present a new approach to quantum error correction based on the same techniques used to conceive the best Go game engine. We showed that efficient correction strategies can be uncovered with evolutionary optimization algorithms, competitive with gradient-based optimization techniques. In particular, we found that shallow neural-networks are competitive with deep neural-networks
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Yang, Jiaying. "Support Vector Machines on Noisy Intermediate-Scale Quantum Computers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266112.
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Support vector machine algorithms are considered essential for the implementationof automation in a radio access network. Specifically, they are critical inthe prediction of the quality of user experience for video streaming based ondevice and network-level metrics. Quantum support vector machine (QSVM)is the quantum analogue of the classical support vector machine algorithm,which utilizes the properties of quantum computers to exponentially speed upthe algorithm. This thesis provides an implementation of the QSVMclassificationsystem and its fundamental components, the quantum Fourier transform(QFT) and the Harrow-Hassidim-Lloyd (HHL) algorithms, using the opensourcequantum computing software development kits (SDKs), IBM’s Qiskitand Rigetti’s pyQuil, and real quantum computers that can be accessed by publiccloud service. Moreover, the QSVM classification system is implementedon a superconducting quantum computer, IBMQX2, showing the potential ofthis quantum algorithm to be executed on the Noisy Intermediate-Scale Quantum(NISQ) computers.Supportvektormaskinalgoritmer anses nödvändiga för implementering av automatiseringi radionätet. De är kritiska när det gäller att säkerställa den upplevdaanvändarkvaliteten för strömmad video (quality of user experience) baseradpå enhets- och nätverksnivåmätningar. Kvantsupportvektormaskinsalgoritmen(QSVM) är en kvantanaloga version av den klassiska supportvektormaskinalgoritmen,som använder egenskaperna hos kvantdatorer för att exponentielltsnabba upp algoritmen. Denna avhandling tillhandahåller en implementeringav den QSVM klassificeringssystemet och dess grundläggande komponenter,kvant-Fourier-transform (QFT) och Harrow-Hassidim-Lloyd (HHL) -algoritmerna, med hjälp av open-source kvantmjukvara (SDK), IBMs Qiskitoch Rigettis pyQuil och riktiga kvantdatorer som kan nås via en offentlig molntjänst.Dessutom implementeras QSVM-klassificeringssystemet på en supraledandekvantdator, IBMQX2, som visar potentialen för denna kvantalgoritmatt kunna exekveras på den brusiga medelstora kvantdatorer (NISQ).
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Wiedmann, Michael [Verfasser]. "Non-Markovian open quantum dynamics from dissipative few-level systems to quantum thermal machines / Michael Wiedmann." Ulm : Universität Ulm, 2020. http://d-nb.info/1204481180/34.
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