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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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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.
3
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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Pesah, Arthur. "Learning quantum state properties with quantum and classical neural networks." Thesis, KTH, Tillämpad fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252693.
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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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Kiani, Bobak Toussi. "Quantum artificial intelligence : learning unitary transformations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127158.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 77-83).
Linear algebra is a simple yet elegant mathematical framework that serves as the mathematical bedrock for many scientific and engineering disciplines. Broadly defined as the study of linear equations represented as vectors and matrices, linear algebra provides a mathematical toolbox for manipulating and controlling many physical systems. For example, linear algebra is central to the modeling of quantum mechanical phenomena and machine learning algorithms. Within the broad landscape of matrices studied in linear algebra, unitary matrices stand apart for their special properties, namely that they preserve norms and have easy to calculate inverses. Interpreted from an algorithmic or control setting, unitary matrices are used to describe and manipulate many physical systems.
Relevant to the current work, unitary matrices are commonly studied in quantum mechanics where they formulate the time evolution of quantum states and in artificial intelligence where they provide a means to construct stable learning algorithms by preserving norms. One natural question that arises when studying unitary matrices is how difficult it is to learn them. Such a question may arise, for example, when one would like to learn the dynamics of a quantum system or apply unitary transformations to data embedded into a machine learning algorithm. In this thesis, I examine the hardness of learning unitary matrices both in the context of deep learning and quantum computation. This work aims to both advance our general mathematical understanding of unitary matrices and provide a framework for integrating unitary matrices into classical or quantum algorithms. Different forms of parameterizing unitary matrices, both in the quantum and classical regimes, are compared in this work.
In general, experiments show that learning an arbitrary dxd² unitary matrix requires at least d² parameters in the learning algorithm regardless of the parameterization considered. In classical (non-quantum) settings, unitary matrices can be constructed by composing products of operators that act on smaller subspaces of the unitary manifold. In the quantum setting, there also exists the possibility of parameterizing unitary matrices in the Hamiltonian setting, where it is shown that repeatedly applying two alternating Hamiltonians is sufficient to learn an arbitrary unitary matrix. Finally, I discuss applications of this work in quantum and deep learning settings. For near term quantum computers, applying a desired set of gates may not be efficiently possible. Instead, desired unitary matrices can be learned from a given set of available gates (similar to ideas discussed in quantum controls).
Understanding the learnability of unitary matrices can also aid efforts to integrate unitary matrices into neural networks and quantum deep learning algorithms. For example, deep learning algorithms implemented in quantum computers may leverage parameterizations discussed here to form layers in a quantum learning architecture.
by Bobak Toussi Kiani.
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
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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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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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Hnatenko, O. S. "Quantum computing. Quantum information technologies as the basis for future learning platforms." Thesis, ISMA University of Applied Science, Riga, Latvia, 2021. https://openarchive.nure.ua/handle/document/16270.
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This paper presents the place of quantum technologies in the modern information world. The technique of quantum computing is described. Also presented is a new model of a qubit based on a nanolaser with frequency stabilization, which emits at different wavelengths, which corresponds to its different states. Thus, the work proposes a scheme of a qubit, which underlies quantum technologies and quantum computers. Quantum computing is a thousand times faster than existing ones. In the future this technology will be able to solve problems that are beyond the power of modern computers, which means it will become the basis for learning and understanding the world more broadly.
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Low, Richard Andrew. "Pseudo-randonmess and Learning in Quantum Computation." Thesis, University of Bristol, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520259.
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Ireson, Gren. "Students' understanding of quantum phenomena." Thesis, Loughborough University, 2000. https://dspace.lboro.ac.uk/2134/7381.
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Sentís, Herrera Gael. "Dealing with ignorance: universal discrimination, learning and quantum correlations." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/134830.
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Una de las tareas fundamentales de la Teor a de la Informaci on Cu antica
consiste en identi car el estado en que ha sido preparado un sistema cu antico.
Cuando las posibles preparaciones forman una lista nita de estados, el problema
recibe el nombre de discriminaci on de estados. El caso fundamental de
unicamente dos estados posibles se conoce tambi en bajo el nombre de contraste
de hip otesis. Una de las caracter sticas distintivas de la teor a cu antica
es el hecho de que para dos estados no ortogonales no hay medida concebible
que pueda identi car el estado del sistema con certeza. El indeterminismo
cu antico exige, por tanto, un enfoque probabil stico para llevar a cabo esta
tarea. Habitualmente se considera que los estados posibles del sistema son
conocidos para el experimentador. En esta tesis analizo el papel que desempe~
na la informaci on previa disponible en la tarea de discriminaci on y, en
particular, analizo situaciones en las que dicha informaci on es incompleta.
Suponiendo una total ignorancia de la identidad de los estados posibles,
estudio la probabilidad de error de una m aquina programable de discriminaci
on para estados de qubit. Esta m aquina incorpora la informaci on sobre
los estados en forma de programas de entrada donde se introducen los sistemas
cu anticos en las diferentes preparaciones. Es decir, la informaci on
es utilizada en su forma genuinamente cu antica, en lugar de como una descripci
on cl asica de los estados. Esta ignorancia cl asica se tiene en cuenta en
el dise~no de la m aquina, la cual ya no es espec ca para cada caso, sino que es
capaz de discriminar entre cualquier par de estados de qubit, una vez ha sido
convenientemente programada mediante las entradas de estados cu anticos.
Estudio en detalle el rendimiento optimo de estas m aquinas para estados de
qubit generales cuando se dispone de un n umero de copias arbitrario, tanto
de los programas como del estado que se ha de identi car. Espec camente,
obtengo las probabilidades de correcta identi caci on en los esquemas usuales de error m nimo y discriminaci on no ambigua, as como en el esquema m as
general de discriminaci on con margen de error.
A continuaci on, este tipo de automatizaci on en tareas de discriminaci on
se lleva un paso m as all a. Entendiendo una m aquina programable como
un dispositivo entrenado con informaci on cu antica que es capaz de realizar
una tarea espec ca, propongo una m aquina de aprendizaje cu antico para
clasi car estados de qubit que no requiere una memoria cu antica para almacenar
los qubits de los programas, permitiendo as repetidos usos de la
m aquina sin necesidad de volver a entrenarla. Demuestro que dicha m aquina
de aprendizaje es capaz de clasi car el estado de un qubit con la m nima
tasa de errores admitida por la mec anica cu antica, y por tanto puede ser
reusada manteniendo un rendimiento optimo. Tambi en estudio un esquema
de aprendizaje similar para estados de luz coherente. Este se presenta en
un contexto de lectura de una memoria cl asica mediante se~nales coherentes
correlacionadas cl asicamente cuando estas son producidas por una fuente imperfecta
y, por lo tanto, en un estado con un cierto grado de incertidumbre
asociado. Muestro que la extracci on de la informaci on almacenada en la
memoria es m as e ciente si la incertidumbre se trata de una forma completamente
cu antica.
Por ultimo, analizo la estructura matem atica de las medidas cu anticas
generalizadas, omnipresentes en todos los temas tratados en esta tesis. Propongo
un algoritmo constructivo y e ciente para descomponer cualquier medida
cu antica en una combinaci on convexa estad sticamente equivalente de
medidas m as simples (extremales). Estas en principio son menos costosas de
implementar en un laboratorio y, por tanto, pueden ser utiles en situaciones
pr acticas donde a menudo prevalece una perspectiva de recursos m nimos.Discriminating the state of a quantum system among a number of options is one of the most fundamental operations in quantum information theory. A primal feature of quantum theory is that, when two possible quantum states are nonorthogonal, no conceivable measurement of the system can determine its state with certainty. Quantum indeterminism so demands a probabilistic approach to the task of discriminating between quantum states. The usual setting considers that the possible states of the system are known. In this thesis, I analyze the role of the prior information available in facing a quantum state discrimination problem, and consider scenarios where the information regarding the possible states is incomplete. In front of a complete ignorance of the possible states' identity, I discuss a quantum programmable discrimination machine for qubit states that accepts this information as input programs using a quantum encoding, rather than just as a classical description. This \classical" ignorance is taken into account in the design, and, as a consequence, the machine is not case-speci c but it is able to handle discrimination tasks between any pair of possible qubits, once conveniently programmed through quantum inputs. The optimal per- formance of programmable machines is studied in detail for general qubit states when several copies of the states are provided, in the main schemes of unambiguous and minimum-error discrimination as well as in the more general scheme of discrimination with an error margin. Then, this type of automation in discrimination tasks is taken further. By realizing a programmable machine as a device that is trained through quantum information to perform a speci c task, I propose a quantum learning machine for classifying qubit states that does not require a quantum memory to store the qubit programs. I prove that such learning machine classi es the state of a qubit with the minimum-error rate that quantum mechanics permits, thus allowing for several optimal uses of the machine without the need of retraining. A similar learning scheme is also discussed for coherent states of light. I present it in the context of the readout of a classical memory by means of classically correlated coherent signals, when these are produced by an imperfect source and hence their state has some uncertainty associated. I show that the retrieval of information stored in the memory can be carried out more accurately when fully general quantum measurements are used. Finally, I analyse the mathematical structure of generalized quantum measurements, ubiquitous in all the topics covered in this thesis. I pro- pose a constructive and e cient algorithm to decompose any given quantum measurement into a statistically equivalent convex combination of simpler (extremal) measurements, which are in principle less costly to implement in a laboratory. Being able to compute this type of measurement decomposi- tions becomes useful in practical situations, where often a minimum-resources perspective prevails.
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Sadaghiani, Homeyra R. "Conceptual and mathematical barriers to students learning quantum mechanics." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1123878116.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xvii, 321 p.; also includes graphics (some col.). Includes bibliographical references. Available online via OhioLINK's ETD Center
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Spada, Roberta. "The second quantum revolution: designing a teaching-learning activity on the quantum manifesto to futurize science education." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18360/.
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Questa tesi è la conclusione di un lavoro all’interno di I SEE (Inclusive STEM Education to Enhance the capacity to aspire and imagine future careers), un progetto europeo Erasmus+ coordinato dall’Università di Bologna e che coinvolge altri sei partner (http://iseeproject.eu). Il mio lavoro ha portato allo sviluppo di un’attività didattica intitolata “Applicazioni e implicazioni dei computer quantistici nella società” che è parte di un modulo I SEE sui computer quantistici.
Progetto e attività mirano a contribuite a due dibattiti nella ricerca sull’educazione scientifica: quello sulla didattica STEM e sulla sua posizione in contesti di ricerca, istituzionali e didattici; quello sulla percezione del futuro da parte dei giovani in questo mondo in accelerazione.
Il primo capitolo riguarda lo stato dell’arte del dibattito sulla didattica STEM, da un punto di vista sia di ricerca che istituzionale, come modo di affrontare temi chiave che riguardano il rapporto problematico tra scienza e società.
Nel secondo capitolo, viene presentato il progetto I SEE e collocato all’interno della ricerca nella didattica STEM. È fornita una descrizione di come tale progetto contribuisce a promuovere lo sviluppo delle cosiddette future-scaffolding skills e a disegnare un approccio STEM integrato, con una descrizione dei moduli finlandese e italiano sulle tecnologie quantistiche.
Il terzo capitolo include la descrizione dell’attività che ho contribuito a sviluppare. Essa è stata costruita per raggiungere diversi obiettivi tra cui guidare gli studenti di scuola secondaria a familiarizzare con la terminologia, le prospettive e i contenuti di documenti istituzionali come il Quantum Manifesto, e rendersi conto delle tante dimensioni coinvolte, riconoscendo dove e come le tecnologie quantistiche potranno essere d’impatto nella vita del singolo.
Infine, si discutono i risultati dell’implementazione dell’attività avvenuta a Bologna nel febbraio 2019 con 25 studenti di scuola secondaria.
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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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Ravaioli, Giovanni. "Learning and accepting quantum physics. Re-analysis of a teaching proposal." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11220/.
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Nella tesi è analizzata nel dettaglio una proposta didattica sulla Fisica Quantistica elaborata dal gruppo di ricerca in Didattica della Fisica dell’Università di Bologna, in collaborazione con il gruppo di ricerca in Fisica Teorica e con ricercatori del CNR di Bologna. La proposta è stata sperimentata in diverse classi V di Liceo scientifico e dalle sperimentazioni sono emersi casi significativi di studenti che non sono riusciti ad accettare la teoria quantistica come descrizione convincente ad affidabile della realtà fisica (casi di non accettazione), nonostante sembrassero aver capito la maggior parte degli argomenti e essersi ‘appropriati’ del percorso per come gli era stato proposto.
Da questa evidenza sono state formulate due domande di ricerca: (1) qual è la natura di questa non accettazione? Rispecchia una presa di posizione epistemologica o è espressione di una mancanza di comprensione profonda? (2) Nel secondo caso, è possibile individuare precisi meccanismi cognitivi che possono ostacolare o facilitare l’accettazione della fisica quantistica?
L’analisi di interviste individuali degli studenti ha permesso di mettere in luce tre principali esigenze cognitive (cognitive needs) che sembrano essere coinvolte nell’accettazione e nell’apprendimento della fisica quantistica: le esigenze di visualizzabilità, comparabilità e di ‘realtà’.
I ‘cognitive needs’ sono stati quindi utilizzati come strumenti di analisi delle diverse proposte didattiche in letteratura e del percorso di Bologna, al fine di metterne in luce le criticità. Sono state infine avanzate alcune proposte per un suo miglioramento.
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Nuuman, Sinan. "Quantum reinforcement learning for dynamic spectrum access in cognitive radio networks." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/15617/.
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This thesis proposes Quantum Reinforcement Learning (QRL) as an improvement to conventional reinforcement learning-based dynamic spectrum access used within cognitive radio networks. The aim is to overcome the slow convergence problem associated with exploration within reinforcement learning schemes. A literature review for the background of the carried out research work is illustrated. Review of research works on learning-based assignment techniques as well as quantum search techniques is provided. Modelling of three traditional dynamic channel assignment techniques is illustrated and the advantage characteristic of each technique is discussed. These techniques have been simulated to provide a comparison with learning based techniques, including QRL. Reinforcement learning techniques are used as a direct comparison with the Quantum Reinforcement Learning approaches. The elements of Quantum computation are then presented as an introduction to quantum search techniques. The Grover search algorithm is introduced. The algorithm is discussed from a theoretical perspective. The Grover algorithm is then used for the first time as a spectrum allocation scheme and compared to conventional schemes. Quantum Reinforcement Learning (QRL) is introduced as a natural evolution of the quantum search. The Grover search algorithm is combined as a decision making mechanism with conventional Reinforcement Learning (RL) algorithms resulting in a more efficient learning engine. Simulation results are provided and discussed. The convergence speed has been significantly increased. The beneficial effects of Quantum Reinforcement Learning (QRL) become more pronounced as the traffic load increases. The thesis shows that both system performance and capacity can be improved. Depending on the traffic load, the system capacity has improved by 9-84% from a number of users supported perspective. It also demonstrated file delay reduction for up to an average of 26% and 2.8% throughput improvement.
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Snook, Michael. "Quantum Resistant Authenticated Key Exchange from Ideal Lattices." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1470757378.
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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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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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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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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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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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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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Wyderka, Nikolai [Verfasser], and Otfried [Gutachter] Gühne. "Learning from correlations: what parts of quantum states tell about the whole / Nikolai Wyderka ; Gutachter: Otfried Gühne." Siegen : Universitätsbibliothek der Universität Siegen, 2020. http://d-nb.info/1211819973/34.
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R, V. Saraswathy. "Zero-Knowledge Proof for Knowledge of RLWE (Ring-Learning with Errors) Secret Keys." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1521192556946491.
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Alsayigh, Saed A. "New Password Authenticated Key Exchange Based on the Ring Learning with Errors." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1468337264.
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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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Johansson, Alexander. "Performance analysis of lattice based post-quantum secure cryptography with Java." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-164306.
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Efficient quantum computers will break most of today’s public-key cryptosystems. Therefore, the National Institute of Standards and Technology (NIST) calls for proposals to standardise one or more quantum-secure cryptographic schemes. Eventually, banks must adopt the standardised schemes, but little is known about how efficient such an implementation would be in Java, one of the standard programming languages for banks. In this thesis, we test and evaluate a post-quantum secure encryption scheme known as FrodoKEM, which is based on a hard lattice problem known as Learning With Errors (LWE). We found that a post-quantum secure encryption version of FrodoKEM provides strong theoretical security regarding the criteria given by NIST, and is also sufficiently fast for key generation, encryption and decryption. These results imply that it could be possible to implement these types of post-quantum secure algorithms in high-level programming languages such as Java, demonstrating that we no longer are limited to use low-level languages such as C. Consequently, we can easier and cheaper implement post-quantum secure cryptography.
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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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Ma, Yunpu [Verfasser], and Volker [Akademischer Betreuer] Tresp. "Learning with relational knowledge in the context of cognition, quantum computing, and causality / Yunpu Ma ; Betreuer: Volker Tresp." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1219852333/34.
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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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Rosca, Georgiana-Miruna. "On algebraic variants of Learning With Errors." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN063.
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La cryptographie à base de réseaux euclidiens repose en grande partie sur l’utilisation du problème Learning With Errors (LWE) comme fondation de sécurité. Ce problème est au moins aussi difficile que les problèmes standards portant sur les réseaux, mais les primitives cryptographiques qui l’utilisent sont inefficaces en termes de consommation en temps et en espace. Les problèmes Polynomial Learning WithErrors (PLWE), dual Ring Learning With Errors (dual-RLWE) et primal Ring Learning With Errors(primal-RLWE) sont trois variantes de LWE qui utilisent des structures algébriques supplémentaires afin de pallier les inconvénients ci-dessus. Le problème PLWE est paramétré par un polynôme f, alors que dual-RLWE et primal-RLWE sont définis à l’aide de l’anneau d’entiers d’un corps de nombres.Ces problèmes, dits algébriques, sont eux-mêmes au moins aussi difficiles que des problèmes standards portant sur les réseaux, mais, dans leur cas, les réseaux impliqués appartiennent à des classes restreintes.Dans cette thèse, nous nous intéressons aux liens entre les variantes algébriques de LWE.Tout d’abord, nous montrons que pour une vaste classe de polynômes de définition, il existe des réductions (non-uniformes) entre dual-RLWE, primal-RLWE et PLWE pour lesquelles l’amplification des paramètres peut être contrôlée. Ces résultats peuvent être interprétés comme une indication forte de l’équivalence calculatoire de ces problèmes.Ensuite, nous introduisons une nouvelle variante algébrique de LWE, Middle-Product Learning WithErrors (MP-LWE). On montre que ce problème est au moins aussi difficile que PLWE pour beaucoup de polynômes de définition f. Par conséquent, un système cryptographique reposant sur MP-LWE reste sûr aussi longtemps qu’une de ces instances de PLWE reste difficile à résoudre.Enfin, nous montrons la pertinence cryptographique de MP-LWE en proposant un protocole de chiffrement asymétrique et une signature digitale dont la sécurité repose sur la difficulté présumée de MP-LWELattice-based cryptography relies in great parts on the use of the Learning With Errors (LWE) problemas hardness foundation. This problem is at least as hard as standard worst-case lattice problems, but the primitives based on it usually have big key sizes and slow algorithms. Polynomial Learning With Errors (PLWE), dual Ring Learning With Errors (dual-RLWE) and primal Ring Learning WithErrors (primal-RLWE) are variants of LWE which make use of extra algebraic structures in order to fix the above drawbacks. The PLWE problem is parameterized by a polynomial f, while dual-RLWE andprimal-RLWE are defined using the ring of integers of a number field. These problems, which we call algebraic, also enjoy reductions from worst-case lattice problems, but in their case, the lattices involved belong to diverse restricted classes. In this thesis, we study relationships between algebraic variants of LWE.We first show that for many defining polynomials, there exist (non-uniform) reductions betweendual-RLWE, primal-RLWE and PLWE that incur limited parameter losses. These results could be interpretedas a strong evidence that these problems are qualitatively equivalent.Then we introduce a new algebraic variant of LWE, Middle-Product Learning With Errors (MP-LWE). We show that this problem is at least as hard as PLWE for many defining polynomials f. As a consequence,any cryptographic system based on MP-LWE remains secure as long as one of these PLWE instances remains hard to solve.Finally, we illustrate the cryptographic relevance of MP-LWE by building a public-key encryption scheme and a digital signature scheme that are proved secure under the MP-LWE hardness assumption
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Johansson, Anders. "Uniformity in physics courses and student diversity : A study of learning to participate in physics." Licentiate thesis, Uppsala universitet, Fysikundervisningens didaktik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-267308.
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This licentiate thesis describes an investigation of participation and achievement in undergraduate physics courses with a discourse analytical lens. Issues of unequal participation have been a growing concern for the physics education research community. At the same time, these issues have not been explored to any large extent using already developed theoretical tools from fields of social science and humanities. This thesis builds on earlier studies in physics education research but crosses disciplinary boundaries to bring in perspectives from gender studies. The two papers use a discourse theoretical framework to explore what it might mean to participate in physics, whether that is one’s primary subject or not, in courses in electromagnetism and quantum physics. A general conclusion that can be drawn from these empirical studies is that physics courses may often be taught from a narrow physics perspective, and that this may limit the possibilities for identification for many students. For instance, engineering students whose main area was not physics failed to see much significance in studying electromagnetism and then just “studied to pass”. Additionally, students on physics programmes may find that the limited positions in quantum physics which can be characterized as mainly focused on “calculating”, are hard to reconcile with their interest in physics. Using a discourse perspective, I broaden this critique to a discussion of the culture of physics: What does it mean to become a physicist and what physics culture follows from different “productions” of physicists? These results inform continued research in physics education by raising issues of identity and providing critical frameworks for exploring them. They also point to the importance of including broad views of physics in courses. Critically examining participation in physics, this thesis aims at widening the discussion and provide new ways to talk about these issues in physics education research.
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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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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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Domert, Daniel. "Explorations of University Physics in Abstract Contexts : From de Sitter Space to Learning Space." Doctoral thesis, Uppsala universitet, Fysikundervisningen didaktik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7265.
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This is a thesis which contributes to research in two different fields: theoretical physics and physics education research. The common link between these two research areas is that both involve explorations of abstract physics and mathematical representations, but from different perspectives. The first part of this thesis is situated in theoretical physics. Here a cosmological scenario is explored where a de Sitter phase is replaced with a phase described with a scale factor a(t) ~ tq, where 1/3<1. This scenario could be viewed as an inflationary toy model, and is shown to open up the possibility of an information paradox. This potential paradox is resolved even in the worst case scenario by showing that the time scales involved for such a paradox to occur is of the order of magnitude of the recurrence time for the de Sitter space. The second part of this thesis is situated in physics education research. A number of learning situations that are experienced as abstract by students are explored: probability in one dimensional quantum tunnelling; the mindsets that students adopt towards understanding physics equations used in typical teaching scenarios; and what students focus on when presented with physics equations. The results for the quantum scattering study are four phenomenographic categories of description, for the mind sets study, six epistemological components of mindsets and for the focus on physics equations study, three foci creating five levels of increasing complexity of ways of experiencing physics equations. Pedagogical implications of these results are discussed.
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Slovinscki, Luciano. "O experimento da borracha quântica : uso de analogias para o entendimento do quântico pelo clássico." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169564.
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As pesquisas sobre o Ensino de Física no Brasil, apoiadas nos documentos oficiais da Educação e na necessidade, cada vez mais perceptível, de atualização dos currículos, indicam e fomentam uma maior articulação de tópicos de Física Moderna e Contemporânea (FMC) nas aulas de Física do Ensino Médio. Baseado nesse panorama, este trabalho sugere uma proposta educacional alicerçada no referencial teórico da aprendizagem significativa de David Ausubel para apresentar uma abordagem qualitativa dos princípios fundamentais da Mecânica Quântica sob uma perspectiva histórica, amparada em fatos científicos que antecederam a interpretação de Erwin Schrödinger da teoria quântica. Os conceitos levados aos estudantes foram baseados nos seis postulados da Mecânica Quântica, e passaram por um processo de transposição didática segundo a teoria de Yves Chevallard, de forma a adaptar os conteúdos ao nível de conhecimento dos estudantes. Como, segundo Ausubel, o fator decisivo para a aprendizagem depende do que o estudante já conhece, os encontros foram pautados, sempre que possível, em analogias entre o quântico e o clássico. Para atingir os objetivos de aprendizagem, lançou-se mão de uma variada gama de experimentos reais e virtuais. O uso de simulações computacionais mostrou-se uma valiosa ferramenta de apoio ao ensino, bem como o experimento da borracha quântica, figura central desta proposta didática, que permitiu a demonstração e a discussão de todos os conceitos tratados. Os resultados obtidos pelos alunos mostraram que boa parte deles conseguiu assimilar aquilo que se pretendeu ensinar, havendo indícios de aprendizagem significativa. A aplicação do projeto se deu no Colégio Militar de Porto Alegre, a uma turma de 36 alunos, majoritariamente do 3º Ano do Ensino Médio, durante o segundo semestre de 2016.Studies about Teaching Physics in Brazil, supported by official documents in Education and by the necessity, increasingly noticeable, of curriculum update, indicate and promote a greater articulation of topics of Modern and Contemporary Physics (MCP) in High School physics classes. Based on this overview, this work suggests an educational proposal underpinned by David Ausubel’s Meaningful Learning theoretical framework to present a qualitative approach of fundamental principles of Quantum Mechanics, from a historical perspective and supported by scientific facts that preceded Erwin Schrödinger's interpretation of quantum theory. The concepts delivered to the students were based on six postulates of Quantum Mechanics and went through a process of didactic transposition according to Yves Chevallard's theory, in a way to adapt the content to students level of knowledge. According to Ausubel’s theory, the decisive factor for learning depends on what the student already knows, and thus the meetings were based, whenever possible, on analogies between the quantum and the classical approach. To reach the learning objectives, we used a wide range of real and virtual experiments. The use of computational simulations proved to be a valuable tool to support teaching. The quantum eraser experiment, in particular, central figure of this didactic proposal, allowed the demonstration and discussion of all the addressed concepts. Results showed that most of students could assimilate what was intended to be taught, suggesting meaningful learning. The project was implemented in the Military College of Porto Alegre to a group of 36 students, mostly from the third year of high school, during the second semester of 2016.
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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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Griebeler, Adriane. "Inserção de tópicos de física quântica no ensino médio através de uma unidade de ensino potencialmente significativa." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/61844.
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Inserir conteúdos de Física Quântica no Ensino Médio torna-se um desafio para os professores, tanto por uma possível falta de preparo como pela falta de incentivo. Apesar da existência de materiais didáticos disponíveis, raramente os professores sentem-se preparados para abordar tais conteúdos em sala de aula. Além disso, o atual currículo do Ensino Médio bem como o programa ENEM, não estimulam ao ensino do assunto aqui referido. No entanto, em função da importância da abordagem da Física Quântica, e também frente à propagação de versões místicas e de representações sociais, é preciso continuar tentando contribuir para que essa inserção venha a ocorrer futuramente. Nesse sentido, foi elaborada uma unidade de ensino sobre tópicos de Física Quântica com a intenção de estimular o interesse e a curiosidade do aluno. Como referencial teórico foi utilizada a teoria de aprendizagem de David Ausubel, buscando desenvolver uma aprendizagem significativa dos conteúdos selecionados da Física Quântica a partir de conhecimentos prévios existentes na estrutura cognitiva dos alunos. A proposta foi elaborada seguindo os passos das Unidades de Ensino Potencialmente Significativas – UEPS (MOREIRA, 2011) onde, num primeiro momento foi feito o levantamento dos conhecimentos prévios dos alunos, em seguida foi apresentado o conteúdo de uma forma mais geral e, a partir daí, cada assunto foi abordado de forma mais específica, visando à diferenciação progressiva e à reconciliação integradora. Os conceitos abordados foram quantização, incerteza, estado e superposição de estados, apresentados de acordo com os passos da UEPS. A implementação do trabalho foi realizada em quatro turmas de 3ª série do Ensino Médio da E. E. E. M. Carlos Antonio Kluwe, em Bagé, RS, durante os meses de outubro, novembro e dezembro de 2011. As quatro turmas, denominadas A, B, C e D, foram divididas em dois grupos. O primeiro grupo, formado pelas turmas A e B iniciou as atividades em 10 de outubro de 2011 e o segundo grupo, formado pelas turmas C e D iniciou as atividades em 31 de outubro. Dentre as atividades desenvolvidas são analisados de forma qualitativa os mapas mentais e mapas conceituais elaborados em duplas, bem como a comparação entre eles, e são mostrados os trabalhos livres confeccionados pelos alunos e o jornal de cada turma Ainda são apresentados alguns comentários de estudantes sobre seu desenvolvimento na compreensão dos conceitos abordados na proposta. A análise feita a partir dos resultados obtidos forneceu indícios de aprendizagem significativa, que é o objetivo de uma UEPS. São apresentados também alguns comentários que indicam a boa receptividade da proposta, que encoraja novas aplicações.It has become a challenge to high school teachers to include contents of quantum physics in the school curriculum not only for the lack of due preparation but for the scarcity of incentive. In spite of available educational resources, teachers seldom feel ready to approach such contents in the classroom. Furthermore, the present high school curriculum as well as the National High School Exam (ENEM) do not seem to encourage the teaching of quantum physics. Nevertheless, because of the relevance of quantum physics and the dissemination of its mystical versions with their social representations, it is necessary to continue attempting at contributing to make this insertion possible in the near future. In this sense, a teaching unit on topics of quantum physics was developed with the purpose of motivating the students ’ interest and curiosity. David Ausubel’s theory of meaningful learning was used a its theoretical framework, so as to facilitate the occurrence of meaningful learning of the chosen contents of quantum physics based on the students’ prior knowledge about the subject. The proposal was developed in agreement with the steps of the Potentially Meaningful Teaching Units – PMTU (Moreira, 2011), according to which the first step was to make a survey of the students’ prior knowledge followed by a general presentation of the contents. From there on, each topic received a more specific/extensive treatment aiming at progressive differentiation and integrative reconciliation. The concepts involved in this study were quantization, uncertainty, state, and superposition of states and they were presented according to the PMTU’s steps. Implementation of this proposal occurred in four different classes of the 3rd year of high school at the State School E.M. Carlos Antonio Kluwe, Bagé, RS, Brazil, from October to December of 2011. These four classes, named A, B, C, and D, were divided into two groups. The first one, formed by classes A and B, started their activities on the October 10, 2011, while the second group, classes C and D, started them on October 31, 2011. The activities performed by the students, such as mind maps and concept maps drawn in pairs, as well as their comparison were qualitatively analysed. There are also shown free-choice works the students produced, as well as the newspaper that each of the classes put together. In addition, some of the students’ comments on their own development regarding the concepts approached in this proposal are presented. The analysis based on obtained results suggested evidence of the occurrence of meaningful learning, which is the aim of a PMTU. Furthermore, some comments that might indicate a fair receptivity to the proposal are also set forth since they seem to encourage new implementations of this proposal.
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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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Hilger, Thaís Rafaela. "Representações sociais da física quântica." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17743.
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Apresenta-se um estudo preliminar a respeito das possíveis representações sociais da Física Quântica entre estudantes de diferentes grupos sociais. Após levantamento prévio sobre associações possíveis para a expressão "Física Quântica", foi elaborado um questionário composto por um teste de associação escrita de conceitos (TAEC) e um teste de associação numérica de conceitos (TANC). Responderam ao questionário 494 pessoas, divididas em três grupos: graduandos no curso de Física, estudantes de outros cursos superiores e alunos de Ensino Médio. As respostas aos questionários foram analisadas utilizando estatística multidimensional, o que permitiu a obtenção de diagramas que refletem a estrutura conceitual compartilhada pelos grupos sociais investigados. À luz dos referenciais teóricos da aprendizagem significativa e das representações sociais, foi possível verificar a existência ou não destas representações, que podem atuar como subsunçores para o ensino de Física, juntamente com outras ideias que compõem a ecologia representacional mental do aprendiz. Algumas dessas ideias podem ter sofrido a influência de meios de divulgação, como mídia, livros e filmes. Esses meios auxiliam na propagação de conceitos científicos - ou quase científicos - influenciam o conhecimento das pessoas, por isso se faz necessário investigar qual o significado atribuído a tais conceitos na elaboração de representações sociais. Os resultados indicam a existência de representações sociais compartilhadas entre sujeitos de alguns grupos investigados, permitindo sua identificação e caracterização. Foram encontrados indícios da influência dos meios de divulgação nas representações sociais dos alunos de Ensino Médio, que devem ser mais bem estudados em trabalhos futuros.A preliminary study regarding possible social representations of quantum physics among students from different social groups is described. Based on an initial survey on word associations to the expression "quantum physics" a questionnaire was constructed including a test of written concept associations (WCA) and a test of numerical concept association (NCA). This questionnaire was answered by 494 students, divided into three groups: physics undergraduate students, high school students, and undergraduate students from subjects other than physics. Data was analysed using the multidimensional scaling technique which provided spatial diagrams that reflect the conceptual structure shared by the investigated social groups. In the theoretical framework of the meaningful learning and social representations theories it was possible to gather evidences of the existence or not of social representations which may act as subsumers in the learning of physics together with other representations that constitute the mental representational ecology of the learners. Some of these representations might be an effect of information provided by books, movies and other media. This media disseminate information regarding scientific - or almost scientific concepts - and influence people's knowledge. Given that previous knowledge is the main variable influencing the acquisition of knew knowledge it is important to investigate the social representations resulting from this dissemination of information. Research findings suggest the existence of social representations about quantum physics shared by some of the subjects and groups studied. Specially in the case of high school students some evidence was obtained about the existence of social representations that should be more explored in future studies.
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Rocha, Carlos Raphael. "Sobre o ensino do conceito de estado em cursos introdutórios de mecânica quântica." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/15281.
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Apresenta-se um estudo desenvolvido em três etapas com o intuito de analisar as principais dificuldades de compreensão pelos alunos de dois importantes conceitos da Mecânica Quântica (MQ): estado quântico e superposição linear. As três etapas da pesquisa consistiram em: elaboração, implementação e avaliação de uma proposta em três minicursos em congressos de ensino de Física nos estados do Rio Grande do Sul e de Santa Catarina; elaboração, apresentação e avaliação de um curso de 18 horas ministrado na Universidade do Vale do Rio dos Sinos, RS; revisão do material apresentado no curso anteriormente mencionado e apresentação, implementação e avaliação de curso de 20 horas na Universidade Federal do Rio Grande do Sul, RS. Sob a luz dos referenciais teóricos da aprendizagem significativa de Ausubel e dos campos conceituais de Vergnaud, criaram-se situações-problema que pudessem promover a aprendizagem significativa dos tópicos apresentados em cada etapa da pesquisa. Constatou-se que as formulações do experimento de dupla fenda e do experimento de Stern-Gerlach chamaram a atenção dos alunos e se constituiram em bons exemplos de aplicação dos conceitos de estado de um sistema quântico, de superposição linear de estados, de colapso do vetor de estado, de possíveis valores de medida e de probabilidades de obtenção dos valores de medida e efetivamente facilitaram a compreensão de muitos destes conceitos. A inclusão de tópicos contemporâneos (Emaranhamento Quântico e Criptografia Quântica) motivou os alunos para o aprendizado dos primeiríssimos princípios da MQ. Nos minicursos foi muito grande o interesse despertado pela proposta nos minicursos, um ponto a favor de sua aplicação, ainda que o curto período de contato com os tópicos apresentados não permitisse a verificação da ocorrência de aprendizagem significativa. Pode-se dizer, contudo, que a abordagem utilizada propiciou um bom início de aprendizado da MQ por parte dos alunos e que outros conceitos essenciais, tais como o de operador linear e de evolução temporal de sistemas quânticos, precisam ser mais trabalhados em atividades futuras.A study, developed in three stages, designed to analyze the main difficulties in understanding two important concepts of Quantum Mechanics (QM) is presented. The main concepts of this study are: quantum state and linear superposition. The three stages of this research are: elaboration, implementation and evaluation of a pedagogical proposal in three short-term courses in educational meetings in the states of Rio Grande do Sul and Santa Catarina; elaboration and presentation of a 18 hours course at the University of Vale do Rio dos Sinos, in São Leopoldo, RS; revision of the material presented in the before mentioned course and presentation of a 20 hours course at the Federal University of Rio Grande do Sul, in Porto Alegre, RS. In the light of Ausubel’s meaningful learning and Vergnaud’s conceptual fields theories, problem-situations were designed seeking the promotion of meaningful learning of the contents presented in the different stages of the research. The formulations of the Stern-Gerlach experiment and the double-slit experiment seemed to motivate the students to learn and were good examples of application of the quantum concepts of the state of the quantum system, linear superposition of states, the collapse of the state vector, the possible values of measurement results and the probability of obtaining the measurement results. Research findings suggest that these experiments facilitated the comprehension of much of these concepts. The inclusion of contemporary topics (Quantum Entanglement and Quantum Cryptography) motivated the students for the learning of the very first principles of QM. The interest on the proposal showed during the short-term courses was very high, a point in favor of this activity, although the short period of contact with the contents presented did not allow the verification of meaningful learning. Although the used approach seemed to be a good way to introduce QM to the students, some other essential concepts need more studies, such as the linear operator and the time evolution of quantum systems.
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Hilger, Thaís Rafaela. "Representações sociais de conceitos de física moderna e contemporânea." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/79586.
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Na perspectiva da aprendizagem significativa, o conhecimento prévio, ou subsunçor, do aprendiz é imprescindível para que ocorra a ancoragem cognitiva. As representações sociais, juntamente com outras ideias que compõem a ecologia representacional mental do aprendiz, podem ser relevantes em relação ao que se pretende aprender, pois atuam como subsunçores. Algumas dessas ideias podem ter sofrido a influência de meios de divulgação, que auxiliam na difusão de conceitos científicos – ou quase científicos – e podem influenciar o conhecimento das pessoas, por isso se faz necessário investigar qual o significado atribuído a tais conceitos na elaboração de representações sociais. À luz dos referenciais teóricos da aprendizagem significativa e das representações sociais, é apresentado um estudo, cujos objetivos são: 1) a partir das representações em relação à Física Quântica, identificadas no trabalho de mestrado, propor uma Unidade de Ensino Potencialmente Significativo (UEPS) para tratar deste tema a fim de investigar a possibilidade de modificação nessas representações; e 2) identificar possíveis representações sociais sobre outros conceitos de Física Moderna e Contemporânea que possam atuar como subsunçores no processo de aprendizagem significativa. Para alcançar os objetivos, a pesquisa é dividida em duas frentes de trabalho: a primeira dá continuidade à investigação do mestrado sobre o conceito de Física Quântica, buscando a evolução representacional, através da análise de mapas mentais e conceituais abordados no decorrer de uma proposta de ensino, e a segunda trata da investigação de outros conceitos que possam ser alvo de representações sociais, por meio de questionários de associação de palavras, para alcançar o grande público, como por exemplo, estudantes de Ensino Médio, universitários, moradores de uma mesma região, que compartilhem a mesma cultura, etc. Os resultados indicam a existência de possíveis representações sociais sobre os temas pesquisados e a possibilidade de mudança representacional utilizando a proposta de ensino sobre Mecânica Quântica.In the perspective of the meaningful learning theory, previous knowledge, or subsumers, are indispensable for cognitive anchoring. Social representations, together with other ideas that compose the learner’s mental representation ecology, might be relevant regarding what is to be learned since they act as subsumers. Some of these ideas might have been influenced by the diffusion media, that help to disseminate scientific concepts – or almost cientifick – and might influence people’s knowledge. That’s why it is necessary to investigate the meaning assigned to these concepts in the construction of social representations. Under the frameworks of meaningful learning and social representations theories a research study was carried out with the following objectives: 1) from the social representations identified in the master’s degree study, regarding Quantum Mechanics, to construct a Potentially Meaningful Teaching Until (PMTU) to deal with this subject and to investigate the possibility of changing such representations; 2) to identify possible social representations about other concepts of Modern an Contemporary Physiscs that might act as subsuming concepts in meaningful learning. To reach these objectives the research was divided into two work fronts: the first one provides some continuation to the study carried out in the master’s degree about the concepts of Quantum Physics, looking for a conceptual evolution, through the use of mental and conceptual maps using during the teaching process, and the second one deals with the research of the other concepts that might be target of social representations, through word association questionnaires, to reach a large audience such as, for exemple, high school students, college students, citizens of a given region that share the same culture, etc. Research findings suggest the existance of possible social representations about the chosen topics and also the possibility of a representational change of them using a didactical proposal to teach Quantum Mechanics.
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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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