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Добірка наукової літератури з теми "Quantum Learning"

Автор: Grafiati
Опубліковано: 10 січня 2023
Оновлено: 28 січня 2023

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Статті в журналах з теми "Quantum Learning"

1

Dunjko, Vedran. "Quantum learning unravels quantum system." Science 376, no. 6598 (June 10, 2022): 1154–55. http://dx.doi.org/10.1126/science.abp9885.

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2

Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Анотація:
Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.
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3

Wiebe, Nathan, Ashish Kapoor, and Krysta M. Svore. "Quantum deep learning." Quantum Information and Computation 16, no. 7&8 (May 2016): 541–87. http://dx.doi.org/10.26421/qic16.7-8-1.

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In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.
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4

Behrman, E. C., J. E. Steck, and M. A. Moustafa. "Learning quantum annealing." Quantum Information and Computation 17, no. 5&6 (April 2017): 460–87. http://dx.doi.org/10.26421/qic17.5-6-6.

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We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate generalizability to larger systems. The amount of additional learning necessary decreases as the size of the system increases. Because current technologies limit measurement of the states of quantum annealing machines to determination of the average spin at each site, we then construct a “broken pathway” between the initial and desired states, at each step of which the average spins are nonzero, and show successful learning of that pathway. Using this technique we show we can direct annealing to multiqubit GHZ and W states, and verify that we have done so. Because quantum neural networks are robust to noise and decoherence we expect our method to be readily implemented experimentally; we show some preliminary results which support this.
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5

Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. "Quantum machine learning." Nature 549, no. 7671 (September 2017): 195–202. http://dx.doi.org/10.1038/nature23474.

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6

Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn. "Quantum Reinforcement Learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 5 (October 2008): 1207–20. http://dx.doi.org/10.1109/tsmcb.2008.925743.

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7

Allcock, Jonathan, and Shengyu Zhang. "Quantum machine learning." National Science Review 6, no. 1 (November 30, 2018): 26–28. http://dx.doi.org/10.1093/nsr/nwy149.

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8

Bisio, Alessandro, Giacomo Mauro DʼAriano, Paolo Perinotti, and Michal Sedlák. "Quantum learning algorithms for quantum measurements." Physics Letters A 375, no. 39 (September 2011): 3425–34. http://dx.doi.org/10.1016/j.physleta.2011.08.002.

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9

Chen, Samuel Yen-Chi, and Shinjae Yoo. "Federated Quantum Machine Learning." Entropy 23, no. 4 (April 13, 2021): 460. http://dx.doi.org/10.3390/e23040460.

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Анотація:
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.
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10

Lukac, Martin, and Marek Perkowski. "Inductive learning of quantum behaviors." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 561–86. http://dx.doi.org/10.2298/fuee0703561l.

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In this paper studied are new concepts of robotic behaviors - deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated with various probabilities. The Occam Razor principle, fundamental to inductive learning, is satisfied in this approach by seeking circuits of reduced complexity. This is illustrated by the synthesis of single output quantum circuits, as we extended the logic synthesis approach to Inductive Machine Learning for the case of learning quantum circuits from behavioral examples.
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Дисертації з теми "Quantum Learning"

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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2

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

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Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified aproach to QLS as Quantum Computing is still being discovered and novel applications are being identified. The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning. Based on algorithmic approach, I first design a Genetic Algorithm for Quantum Logic Synthesis that is used to prove and verify the methods proposed in this work. Based on results obtained from the evolutionary experimentation, I propose a fast, structure and cost based exhaustive search that is used for the design of a novel, least expensive universal family of quantum gates. The results form both the evolutionary and heuristic search are used to formulate an Inductive Learning Approach based on Quantum Logic Synthesis with the intended application being the humanoid behavioral robotics. The presented approach illustrates a successful algorithmic approach, where the search algorithm was able to invent/discover novel quantum circuits as well as novel principles in Quantum Logic Synthesis.
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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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4

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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5

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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6

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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Анотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
Cataloged 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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7

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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Анотація:
Orientador: Mario Leandro Aolita
Banca: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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8

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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Анотація:
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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9

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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10

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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Книги з теми "Quantum Learning"

1

Mike, Hernacki, ed. Quantum business: Achieving success through quantum learning. New York: Dell, 1997.

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2

Pattanayak, Santanu. Quantum Machine Learning with Python. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2.

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3

Schuld, Maria, and Francesco Petruccione. Machine Learning with Quantum Computers. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83098-4.

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4

Schuld, Maria, and Francesco Petruccione. Supervised Learning with Quantum Computers. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9.

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5

Schütt, Kristof T., Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and Klaus-Robert Müller, eds. Machine Learning Meets Quantum Physics. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7.

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6

Quantum learning & instructional leadership in practice. Thousand Oaks, CA: Corwin Press, 2007.

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7

Ganguly, Santanu. Quantum Machine Learning: An Applied Approach. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1.

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8

Pastorello, Davide. Concise Guide to Quantum Machine Learning. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6.

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9

Mike, Hernacki, ed. Quantum learning: Unleashing the genius in you. New York, N.Y: Dell Publishing, 1992.

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10

Mike, Hernacki, ed. Quantum learning: Unleash the genius within you. London: Piatkus, 1993.

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Частини книг з теми "Quantum Learning"

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Stretton, Paul. "Learning from Everything." In Quantum Safety, 91–100. New York: Productivity Press, 2022. http://dx.doi.org/10.4324/9781003175742-8.

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Dong, Daoyi, Chunlin Chen, and Zonghai Chen. "Quantum Reinforcement Learning." In Lecture Notes in Computer Science, 686–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539117_97.

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Ganguly, Santanu. "Deep Quantum Learning." In Quantum Machine Learning: An Applied Approach, 403–59. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_8.

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Pattanayak, Santanu. "Quantum Deep Learning." In Quantum Machine Learning with Python, 281–306. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2_6.

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Pattanayak, Santanu. "Quantum Machine Learning." In Quantum Machine Learning with Python, 221–79. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6522-2_5.

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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Mining, 1–10. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_913-1.

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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Mining, 1034–43. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_913.

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Kunczik, Leonhard. "Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing." In Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context, 41–48. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37616-1_4.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology, 21–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96424-9_2.

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Schuld, Maria, and Francesco Petruccione. "Machine Learning." In Quantum Science and Technology, 23–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83098-4_2.

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Тези доповідей конференцій з теми "Quantum Learning"

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Koutný, Dominik, Laia Ginés, Magdalena Moczała-Dusanowska, Sven Höfling, Christian Schneider, Ana Predojević, and Miroslav Ježek. "Deep Learning of Quantum Entanglement." In Quantum 2.0. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/quantum.2022.qth2a.4.

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Анотація:
Detection of quantum correlations in a physical system is paramount to many cutting-edge applications in quantum information processing. We tackle the problem of inferring the entanglement from incomplete measurements by employing deep neural networks.
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Iranzo, Rosa M. Gil, Mercè Teixidó Cairol, Carina González González, and Roberto García. "Learning Quantum Computing." In Interacción '21: XXI International Conference on Human Computer Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3471391.3471424.

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Kudyshev, Zhaxylyk, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev. "Machine Learning Assisted Quantum Photonics." In Quantum 2.0. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/quantum.2020.qm6b.3.

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Wilson, Blake, Yuheng Chen, Sabre Kais, Alexander Kildishev, Vladimir Shalaev, and Alexandra Boltasseva. "Empowering Quantum 2.0 Devices and Approaches with Machine Learning." In Quantum 2.0. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/quantum.2022.qtu2a.13.

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Анотація:
We present recent advances and future perspectives in using machine learning for characterization, fabrication, and inverse design for device applications, such as hybrid quantum-classical optimization of nanostructures, hypothesis learning for automated discovery, and pre-characterization binning.
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Banchi, Leonardo, and Stefano Pirandola. "Supervised Quantum Learning as Quantum Channel Simulation." In Quantum Information and Measurement. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/qim.2019.s4b.5.

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Banchi, Leonardo, and Stefano Pirandola. "Supervised Quantum Learning as Quantum Channel Simulation." In Quantum Information and Measurement. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/qim.2019.s4d.6.

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Nguyen, Tuyen, Incheon Paik, Hiroyuki Sagawa, and Truong Cong Thang. "Quantum Machine Learning with Quantum Image Representations." In 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022. http://dx.doi.org/10.1109/qce53715.2022.00142.

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Liu, Ding, and Minghu Jiang. "Learning quantum operator by quantum adiabatic computation." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7014970.

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Quiroga, David, Prasanna Date, and Raphael Pooser. "Discriminating Quantum States with Quantum Machine Learning." In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2021. http://dx.doi.org/10.1109/qce52317.2021.00088.

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Quiroga, David, Prasanna Date, and Raphael Pooser. "Discriminating Quantum States with Quantum Machine Learning." In 2021 International Conference on Rebooting Computing (ICRC). IEEE, 2021. http://dx.doi.org/10.1109/icrc53822.2021.00018.

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Звіти організацій з теми "Quantum Learning"

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Lukac, Martin. Quantum Inductive Learning and Quantum Logic Synthesis. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2316.

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Zahorodk, Pavlo V., Yevhenii O. Modlo, Olga O. Kalinichenko, Tetiana V. Selivanova, and Serhiy O. Semerikov. Quantum enhanced machine learning: An overview. CEUR Workshop Proceedings, March 2021. http://dx.doi.org/10.31812/123456789/4357.

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Анотація:
Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer.
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Tretiak, Sergei, Benjamin Tyler Nebgen, Justin Steven Smith, Nicholas Edward Lubbers, and Andrey Lokhov. Machine Learning for Quantum Mechanical Materials Properties. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1498000.

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Guy, Khalil, and Gabriel Perdue. Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise. Office of Scientific and Technical Information (OSTI), August 2020. http://dx.doi.org/10.2172/1661681.

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Liu, Minzhao, Ge Dong, Kyle Felker, Matthew Otten, Prasanna Balaprakash, William Tang, and Yuri Alexeev. Exploration of Quantum Machine Learning and AI Accelerators for Fusion Science. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1840522.

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Billari, Francesco C., Johannes Fürnkranz, and Alexia Prskawetz. Timing, sequencing and quantum of life course events: a machine learning approach. Rostock: Max Planck Institute for Demographic Research, October 2000. http://dx.doi.org/10.4054/mpidr-wp-2000-010.

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Farhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.

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Анотація:
We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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Wachen, John, and Steven McGee. Qubit by Qubit’s Middle School Quantum Camp Evaluation Report for Summer 2021. The Learning Partnership, August 2021. http://dx.doi.org/10.51420/report.2021.5.

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Qubit by Qubit’s Middle School Quantum Camp is one of the first opportunities for students as young as eleven to begin learning about the field of quantum computing. In this week-long summer camp, students learn about key concepts of quantum mechanics and quantum computing, including qubits, superposition, and entanglement, basic coding in Python, and quantum gates. By the end of the camp, students can code quantum circuits and run them on a real quantum computer. The Middle School Quantum Camp substantially increased participants’ knowledge about quantum computing, as exhibited by large gains on a technical assessment that was administered at the beginning and end of the program. On a survey of student motivation, students in the program showed a statistically significant increase in their expectancy of being successful in quantum computing and valuing quantum computing. Students experienced a significant increase in their sense of belonging in STEM and quantum computing following the camp. The camp substantially increased students’ interest in taking additional coursework in STEM and quantum, as well as pursuing careers in STEM and quantum computing.
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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Анотація:
Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Lockheed Martin Quantum Machine Learning. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1826570.

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