Relevant bibliographies by topics / Quantum machine

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Quantum machine'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 March 2023

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Journal articles on the topic "Quantum machine"

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Chiara, Maria Luisa Dalla, Roberto Giuntini, Giuseppe Sergioli, and Roberto Leporini. "Abstract quantum computing machines and quantum computational logics." International Journal of Quantum Information 14, no. 04 (June 2016): 1640019. http://dx.doi.org/10.1142/s0219749916400190.

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Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.
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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (November 16, 2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
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TARASOV, VASILY E. "QUANTUM NANOTECHNOLOGY." International Journal of Nanoscience 08, no. 04n05 (August 2009): 337–44. http://dx.doi.org/10.1142/s0219581x09005517.

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Nanotechnology is based on manipulations of individual atoms and molecules to build complex atomic structures. Quantum nanotechnology is a broad concept that deals with a manipulation of individual quantum states of atoms and molecules. Quantum nanotechnology differs from nanotechnology as a quantum computer differs from a classical molecular computer. The nanotechnology deals with a manipulation of quantum states in bulk rather than individually. In this paper, we define the main notions of quantum nanotechnology. Quantum analogs of assemblers, replicators and self-reproducing machines are discussed. We prove the possibility of realizing these analogs. A self-cloning (self-reproducing) quantum machine is a quantum machine which can make a copy of itself. The impossibility of ideally cloning an unknown quantum state is one of the basic rules of quantum theory. We prove that quantum machines cannot be self-cloning if they are Hamiltonian. There exist quantum non-Hamiltonian machines that are self-cloning machines. Quantum nanotechnology allows us to build quantum nanomachines. These nanomachines are not only small machines of nanosize. Quantum nanomachines should use new (quantum) principles of work.
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Peleshenko, Vitaly A. "QUANTUM MACHINE LEARNING." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 82–107. http://dx.doi.org/10.36871/2618-9976.2022.11.008.

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YU, YANG, and LIU YE. "PROPOSAL FOR A GENERAL QUANTUM CLONING MACHINE VIA DISTANT QUBITS IN A QUANTUM NETWORK." International Journal of Modern Physics B 27, no. 23 (August 21, 2013): 1350154. http://dx.doi.org/10.1142/s0217979213501543.

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We propose a scheme for implementing a general quantum cloning machine with distant qubits. By regulating some times and phases, we can easily realize almost all kinds of quantum cloning machines (optimal phase-covariant cloning, optimal universal quantum cloning, optimal economical phase-covariant cloning and optimal real state cloning). The quantum cloning machine may have important applications in quantum cryptography for quantum network.
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CASTAGNOLI, GIUSEPPE. "QUANTUM STEADY COMPUTATION." International Journal of Modern Physics B 05, no. 13 (August 10, 1991): 2253–69. http://dx.doi.org/10.1142/s0217979291000870.

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Current conceptions of "quantum mechanical computers" inherit from conventional digital machines two apparently interacting features, machine imperfection and temporal development of the computational process. On account of machine imperfection, the process would become ideally reversible only in the limiting case of zero speed. Therefore the process is irreversible in practice and cannot be considered to be a fundamental quantum one. By giving up classical features and using a linear, reversible and non-sequential representation of the computational process — not realizable in classical machines — the process can be identified with the mathematical form of a quantum steady state. This form of steady quantum computation would seem to have an important bearing on the notion of cognition.
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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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Acampora, Giovanni. "Quantum machine intelligence." Quantum Machine Intelligence 1, no. 1-2 (May 15, 2019): 1–3. http://dx.doi.org/10.1007/s42484-019-00006-5.

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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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Molina, Abel, and John Watrous. "Revisiting the simulation of quantum Turing machines by quantum circuits." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2226 (June 2019): 20180767. http://dx.doi.org/10.1098/rspa.2018.0767.

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Yao's 1995 publication ‘Quantum circuit complexity’ in Proceedings of the 34th Annual IEEE Symposium on Foundations of Computer Science , pp. 352–361, proved that quantum Turing machines and quantum circuits are polynomially equivalent computational models: t ≥ n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t , and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time. We revisit the simulation of quantum Turing machines with uniformly generated quantum circuits, which is the more challenging of the two simulation tasks, and present a variation on the simulation method employed by Yao together with an analysis of it. This analysis reveals that the simulation of quantum Turing machines can be performed by quantum circuits having depth linear in t , rather than quadratic depth, and can be extended to variants of quantum Turing machines, such as ones having multi-dimensional tapes. Our analysis is based on an extension of method described by Arright, Nesme and Werner in 2011 in Journal of Computer and System Sciences 77 , 372–378. ( doi:10.1016/j.jcss.2010.05.004 ), that allows for the localization of causal unitary evolutions.
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Dissertations / Theses on the topic "Quantum machine"

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

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Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and we are on the verge of adopting some quantum technologies in our daily life. Quantum many-body physics is a subfield of quantum physics where we study the collective behavior of particles or atoms and the emergence of phenomena that are due to this collective behavior, such as phases of matter. The study of phase transitions of these systems often requires some intuition of how we can quantify the order parameter of a phase. ML algorithms can imitate something similar to intuition by inferring knowledge from example data. They can, therefore, discover patterns that are invisible to the human eye, which makes them excellent candidates to study phase transitions. At the same time, quantum devices are known to be able to perform some computational task exponentially faster than classical computers and they are able to produce data patterns that are hard to simulate on classical computers. Therefore, there is the hope that ML algorithms run on quantum devices show an advantage over their classical analog. This thesis is devoted to study two different paths along the front lines of ML and quantum physics. On one side, we study the use of neural networks (NN) to classify phases of mater in many-body quantum systems. On the other side, we study ML algorithms that run on quantum computers. The connection between ML for quantum physics and quantum physics for ML in this thesis is an emerging subfield in ML, the interpretability of learning algorithms. A crucial ingredient in the study of phase transitions with NNs is a better understanding of the predictions of the NN, to eventually infer a model of the quantum system and interpretability can assist us in this endeavor. The interpretability method that we study analyzes the influence of the training points on a test prediction and it depends on the curvature of the NN loss landscape. This further inspired an in-depth study of the loss of quantum machine learning (QML) applications which we as well will discuss. In this thesis, we give answers to the questions of how we can leverage NNs to classify phases of matter and we use a method that allows to do domain adaptation to transfer the learned "intuition" from systems without noise onto systems with noise. To map the phase diagram of quantum many-body systems in a fully unsupervised manner, we study a method known from anomaly detection that allows us to reduce the human input to a mini mum. We will as well use interpretability methods to study NNs that are trained to distinguish phases of matter to understand if the NNs are learning something similar to an order parameter and if their way of learning can be made more accessible to humans. And finally, inspired by the interpretability of classical NNs, we develop tools to study the loss landscapes of variational quantum circuits to identify possible differences between classical and quantum ML algorithms that might be leveraged for a quantum advantage.
La investigación en la intersección del aprendizaje automático (machine learning, ML) y la física cuántica es una área en crecimiento reciente debido al éxito y las enormes expectativas de ambas áreas. ML es posiblemente una de las tecnologías más prometedoras que ha alterado y seguirá alterando muchos aspectos de nuestras vidas. Es casi seguro que la forma en que investigamos no es una excepción y el ML, con su capacidad sin precedentes para encontrar patrones ocultos en los datos ayudará a futuros descubrimientos científicos. La física cuántica, por otro lado, aunque a veces no es del todo intuitiva, es una de las teorías físicas más exitosas, y además estamos a punto de adoptar algunas tecnologías cuánticas en nuestra vida diaria. La física cuántica de los muchos cuerpos (many-body) es una subárea de la física cuántica donde estudiamos el comportamiento colectivo de partículas o átomos y la aparición de fenómenos que se deben a este comportamiento colectivo, como las fases de la materia. El estudio de las transiciones de fase de estos sistemas a menudo requiere cierta intuición de cómo podemos cuantificar el parámetro de orden de una fase. Los algoritmos de ML pueden imitar algo similar a la intuición al inferir conocimientos a partir de datos de ejemplo. Por lo tanto, pueden descubrir patrones que son invisibles para el ojo humano, lo que los convierte en excelentes candidatos para estudiar las transiciones de fase. Al mismo tiempo, se sabe que los dispositivos cuánticos pueden realizar algunas tareas computacionales exponencialmente más rápido que los ordenadores clásicos y pueden producir patrones de datos que son difíciles de simular en los ordenadores clásicos. Por lo tanto, existe la esperanza de que los algoritmos ML que se ejecutan en dispositivos cuánticos muestren una ventaja sobre su analógico clásico. Estudiamos dos caminos diferentes a lo largo de la vanguardia del ML y la física cuántica. Por un lado, estudiamos el uso de redes neuronales (neural network, NN) para clasificar las fases de la materia en sistemas cuánticos de muchos cuerpos. Por otro lado, estudiamos los algoritmos ML que se ejecutan en ordenadores cuánticos. La conexión entre ML para la física cuántica y la física cuántica para ML en esta tesis es un subárea emergente en ML: la interpretabilidad de los algoritmos de aprendizaje. Un ingrediente crucial en el estudio de las transiciones de fase con NN es una mejor comprensión de las predicciones de la NN, para inferir un modelo del sistema cuántico. Así pues, la interpretabilidad de la NN puede ayudarnos en este esfuerzo. El estudio de la interpretabilitad inspiró además un estudio en profundidad de la pérdida de aplicaciones de aprendizaje automático cuántico (quantum machine learning, QML) que también discutiremos. En esta tesis damos respuesta a las preguntas de cómo podemos aprovechar las NN para clasificar las fases de la materia y utilizamos un método que permite hacer una adaptación de dominio para transferir la "intuición" aprendida de sistemas sin ruido a sistemas con ruido. Para mapear el diagrama de fase de los sistemas cuánticos de muchos cuerpos de una manera totalmente no supervisada, estudiamos un método conocido de detección de anomalías que nos permite reducir la entrada humana al mínimo. También usaremos métodos de interpretabilidad para estudiar las NN que están entrenadas para distinguir fases de la materia para comprender si las NN están aprendiendo algo similar a un parámetro de orden y si su forma de aprendizaje puede ser más accesible para los humanos. Y finalmente, inspirados por la interpretabilidad de las NN clásicas, desarrollamos herramientas para estudiar los paisajes de pérdida de los circuitos cuánticos variacionales para identificar posibles diferencias entre los algoritmos ML clásicos y cuánticos que podrían aprovecharse para obtener una ventaja cuántica.
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Müller, Markus. "Quantum Kolmogorov complexity and the quantum turing machine." [S.l.] : [s.n.], 2007. http://opus.kobv.de/tuberlin/volltexte/2007/1655.

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

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Il Quantum Computing (QC) e il Machine Learning (ML) sono due dei settori più promettenti dell’informatica al giorno d’oggi. Il primo riguarda l’utilizzo di proprietà fisiche di sistemi quantistici per realizzare computazioni, mentre il secondo algoritmi di apprendimento automatizzati capaci di riconoscere pattern nei dati. In questo elaborato vengono esposti alcuni dei principali algoritmi di Quantum Machine Learning (QML), ovvero versioni quantistiche dei classici algoritmi di ML. Il tutto è strutturato come un’introduzione all’argomento: inizialmente viene introdotto il QC spiegandone le proprietà più rilevanti, successivamente vengono descritti gli algoritmi di QML confrontandoli con le loro controparti classiche e infine vengono discusse le principali tecnologie attuali, mostrando alcune implementazioni degli algoritmi precedentemente discussi.
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Vicente, Nieto Irene. "Towards Machine Translation with Quantum Computers." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196602.

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This work explores the possibilities to use quantum computers and quantum based language models for machine translation. Precise translation requires vast expertise and knowledge of various languages, thus machine translationis still far from superseding humans. Quantum computers could improve machine translation due to their high computational power, as they benefit from properties such as superposition and entanglement to process data faster and in parallel. We focused our work on the DIStributional COmpositional CATegorical (DisCoCat) semantics and its python toolbox DisCoPy developed by [1]. We built and transformed simple, complex, and negative English and Spanish sentences to DisCoCat diagrams. Those diagrams are then used as input to quantum circuits, allowing us to perform calculations in NISQ devices providedby IBMQ. The calculations show that a quantum computer can understand the meaning of simple and complex sentences in different languages, and this is the first step to perform translation with Quantum Computers. In addition, we worked on preserving sentence meaning by measuring the cosine similarity between two vectorised sentences and obtained sentence similarities scores of 95%.
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Du, Yuxuan. "The Power of Quantum Neural Networks in The Noisy Intermediate-Scale Quantum Era." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24976.

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Machine learning (ML) has revolutionized the world in recent years. Despite the success, the huge computational overhead required by ML models makes them approach the limits of Moore’s law. Quantum machine learning (QML) is a promising way to conquer this issue, empowered by Google's demonstration of quantum computational supremacy. Meanwhile, another cornerstone in QML is validating that quantum neural networks (QNNs) implemented on the noisy intermediate-scale quantum (NISQ) chips can accomplish classification and image generation tasks. Despite the experimental progress, little is known about the theoretical advances of QNNs. In this thesis, we explore the power of QNNs to fill this knowledge gap. First, we consider the potential advantages of QNNs in generative learning. We demonstrate that QNNs possess a stronger expressive power than that of classical neural networks in the measure of computational complexity and entanglement entropy. Moreover, we employ QNNs to tackle synthetic generation tasks with state-of-the-art performance. Next, we propose a Grover-search based quantum classifier, which can tackle specific classification tasks with quadratic runtime speedups. Furthermore, we exhibit that the proposed scheme allows batch gradient descent optimization, which is different from previous studies. This property is crucial to train large-scale datasets. Then, we study the capabilities and limitations of QNNs in the view of optimization theory and learning theory. The achieved results imply that a large system noise can destroy the trainability of QNNs. Meanwhile, we show that QNNs can tackle parity learning and juntas learning with provable advantages. Last, we devise a quantum auto-ML scheme to enhance the trainability QNNs under the NISQ setting. The achieved results indicate that our proposal effectively mitigates system noise and alleviates barren plateaus for both conventional machine learning and quantum chemistry tasks.
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TACCHINO, FRANCESCO. "Digital quantum simulations and machine learning on near-term quantum processors." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1317093.

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Quantum mechanics is the gateway towards novel and potentially disruptive approaches to scientific and technical computing. In this thesis we explore, from a number of different perspectives, the effects of such strong relationship between the physical nature of information and the informational side of physical processes, with a focus on the digital quantum computing paradigm. After an extensive introduction to the theory of universal quantum simulation techniques, we review the main achievements in the field and, in parallel, we outline the state of the art of near-term architectures for quantum information processing. We then move on to present novel and scalable procedures for the study of paradigmatic spin models on intermediate-scale noisy quantum processors. Through an innovative combination of quantum algorithms with classical post-processing and error mitigation protocols, we demonstrate in practice the full digital quantum simulation of spin-spin dynamical correlation functions, reporting experimental results obtained on superconducting cloud-accessible IBM Q devices. We also exhibit a practical use-case by successfully reproducing, from quantum computed data, cross section calculations for four-dimensional inelastic neutron scattering, a common tool employed in the analysis of molecular magnetic clusters. The central part of the thesis is dedicated to the exploration of perspective hardware solutions for quantum computing. As it is not yet clear whether the currently dominant platforms, namely trapped ions and superconducting circuits, will eventually allow to reach the final goal of a fully-fledged architecture for general-purpose quantum information processing, the search for alternative technologies is at least as urgent as the improvement of existing ones or the development of new algorithms. After providing an overview of some recent proposals, including hybrid set-ups, we introduce quantum electromechanics as a promising candidate platform for future realizations of digital quantum simulators and we predict competitive performances for an elementary building block featuring nanomechanical qubits integrated within superconducting circuitry. In the final part, we extend the reach of quantum information protocols beyond its traditional areas of application, and we account for the birth and rapid development of Quantum Machine Learning, a discipline aimed at establishing a productive interplay between the parallel revolutions brought about by quantum computing and artificial intelligence. In particular, we describe an original procedure for implementing, on a quantum architecture, the behavior of binary-valued artificial neurons. Formally exact and platform-independent, our data encoding and processing scheme guarantees in principle an exponential memory advantage over classical counterparts and is particularly well suited for pattern and image recognition purposes. We test our algorithm on IBM Q quantum processors, discussing possible training schemes for single nodes and reporting a proof-of-principle demonstration of a 2-layer, 3-neuron feed-forward neural network computation run on 7 active qubits. The latter is, in terms of the total size of the quantum register, one of the largest quantum neural network computation reported to date on real quantum hardware.
Quantum mechanics is the gateway towards novel and potentially disruptive approaches to scientific and technical computing. In this thesis we explore, from a number of different perspectives, the effects of such strong relationship between the physical nature of information and the informational side of physical processes, with a focus on the digital quantum computing paradigm. After an extensive introduction to the theory of universal quantum simulation techniques, we review the main achievements in the field and, in parallel, we outline the state of the art of near-term architectures for quantum information processing. We then move on to present novel and scalable procedures for the study of paradigmatic spin models on intermediate-scale noisy quantum processors. Through an innovative combination of quantum algorithms with classical post-processing and error mitigation protocols, we demonstrate in practice the full digital quantum simulation of spin-spin dynamical correlation functions, reporting experimental results obtained on superconducting cloud-accessible IBM Q devices. We also exhibit a practical use-case by successfully reproducing, from quantum computed data, cross section calculations for four-dimensional inelastic neutron scattering, a common tool employed in the analysis of molecular magnetic clusters. The central part of the thesis is dedicated to the exploration of perspective hardware solutions for quantum computing. As it is not yet clear whether the currently dominant platforms, namely trapped ions and superconducting circuits, will eventually allow to reach the final goal of a fully-fledged architecture for general-purpose quantum information processing, the search for alternative technologies is at least as urgent as the improvement of existing ones or the development of new algorithms. After providing an overview of some recent proposals, including hybrid set-ups, we introduce quantum electromechanics as a promising candidate platform for future realizations of digital quantum simulators and we predict competitive performances for an elementary building block featuring nanomechanical qubits integrated within superconducting circuitry. In the final part, we extend the reach of quantum information protocols beyond its traditional areas of application, and we account for the birth and rapid development of Quantum Machine Learning, a discipline aimed at establishing a productive interplay between the parallel revolutions brought about by quantum computing and artificial intelligence. In particular, we describe an original procedure for implementing, on a quantum architecture, the behavior of binary-valued artificial neurons. Formally exact and platform-independent, our data encoding and processing scheme guarantees in principle an exponential memory advantage over classical counterparts and is particularly well suited for pattern and image recognition purposes. We test our algorithm on IBM Q quantum processors, discussing possible training schemes for single nodes and reporting a proof-of-principle demonstration of a 2-layer, 3-neuron feed-forward neural network computation run on 7 active qubits. The latter is, in terms of the total size of the quantum register, one of the largest quantum neural network computation reported to date on real quantum hardware.
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Sjöstrand, Joachim. "Engineering superconducting qubits : towards a quantum machine." Doctoral thesis, Stockholm University, Department of Physics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-818.

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A quantum computer is an information processing machine, much like an ordinary classical computer, but its function is based on quantum mechanical principles. To be able to construct such a machine would be a fantastic accomplishment---to have total control over a quantum system is a dream for both physicists and science-fiction enthusiasts. The basic information unit in a quantum computer is the quantum bit, or qubit for short. A quantum computer consists of many coupled qubits. To get a single qubit to work properly, would be a major step towards building this machine.

Here we study two different qubit ideas. The central element in both setups is the superconducting tunnel junction---the Josephson junction. By connecting the Josephson junctions to standard electronics in a clever way, a qubit can be realised. With these constructions it is in principle very easy to manipulate and read out the quantum probabilities, by varying voltages and currents in time. However, this ease of manipulation has a cost: strong interactions with uncontrolled degrees of freedom of the environment transfer information from the qubit. For superconducting qubits this decoherence is typically very fast.

There are ways to deal with the decoherence. One way is to tune the circuit parameters so that the decoherence becomes minimal. Another way is to engineer the qubits so fast so that the effect of decoherence becomes small. In this thesis, we will apply both these strategies. Specifically, the measurement speed of the second qubit we study, turns out to be very sensitive to the topology of the phase space of the detector variables.

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Sjöstrand, Joachim. "Engineering superconducting qubits : towards a quantum machine /." Stockholm : Department of Physics, Stockholm University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-818.

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

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Quantum computation is an emerging computing paradigm with the potential to revolutionise the world of information technology. It leverages the laws of quantum mechanics to endow quantum machines with tremendous computing power, thus enabling the solution of problems impossible to address with classical devices. For this reason, the field is attracting ever-increasing attention from both academic and private sectors, and its full potential is still to be understood. This dissertation investigates how classical machine learning can benefit from quantum computing and provides several contributions to the emerging field of Quantum Machine Learning. The idea is to provide a universal and efficient framework that can reproduce the output of a plethora of classical machine learning algorithms exploiting quantum computation’s advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple functions to solve typical supervised learning tasks. Thanks to this property, in its general formulation MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions. The theoretical design of the quantum algorithm and the corresponding circuit’s implementation are presented. As a second meaningful addition, two practical applications are illustrated: the quantum version of ensemble methods and neural networks. The final contribution addresses the restriction to linear operations imposed by quantum mechanics. The idea is to exploit a quantum transposition of classical Splines to approximate non-linear functions, thus overcoming this limitation and introducing significant advantages in terms of computational complexity theory.
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Orazi, Filippo. "Quantum machine learning: development and evaluation of the Multiple Aggregator Quantum Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25062/.

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Human society has always been shaped by its technology, so much that even ages and parts of our history are often named after the discoveries of that time. The growth of modern society is largely derived from the introduction of classical computers that brought us innovations like repeated tasks automatization and long-distance communication. However, this explosive technological advancement could be subjected to a heavy stop when computers reach physical limitations and the empirical law known as Moore Law comes to an end. Foreshadowing these limits and hoping for an even more powerful technology, forty years ago the branch of quantum computation was born. Quantum computation uses at its advantage the same quantum effects that could stop the progress of traditional computation and aim to deliver hardware and software capable of even greater computational power. In this context, this thesis presents the implementation of a quantum variational machine learning algorithm called quantum single-layer perceptron. We start by briefly explaining the foundation of quantum computing and machine learning, to later dive into the theoretical approach of the multiple aggregator quantum algorithms, and finally deliver a versatile implementation of the quantum counterparts of a single hidden layer perceptron. To conclude we train the model to perform binary classification using standard benchmark datasets, alongside three baseline quantum machine learning models taken from the literature. We then perform tests on both simulated quantum hardware and real devices to compare the performances of the various models.
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Books on the topic "Quantum machine"

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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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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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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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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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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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Odaibo, Stephen G. Quantum mechanics and the MRI machine. Arlington, VA, U.S.A: Symmetry Seed Books, 2012.

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1958-, Shen A., and Vyalyi M. N. 1961-, eds. Classical and quantum computation. Providence, R.I: American Mathematical Society, 2002.

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Kitaev, A. Yu. Classical and quantum computation. Providence, R.I: American Mathematical Society, 2002.

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1958-, Shen A., and Vyalyi M. N. 1961-, eds. Classical and quantum computation. Providence, R.I: American Mathematical Society, 2002.

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Vos, Alexis de. Reversible computing: Fundamentals, quantum computing, and applications. Weinheim: Wiley-VCH, 2010.

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Book chapters on the topic "Quantum machine"

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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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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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Moret-Bonillo, Vicente. "Feynman’s Quantum Computer Machine." In Adventures in Computer Science, 119–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64807-1_6.

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Neumann, Niels M. P., and Robert S. Wezeman. "Distributed Quantum Machine Learning." In Innovations for Community Services, 281–93. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06668-9_20.

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Leider, Avery, Gio Abou Jaoude, Abigail E. Strobel, and Pauline Mosley. "Quantum Machine Learning Classifier." In Lecture Notes in Networks and Systems, 459–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98012-2_34.

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Tandon, Prateek, Stanley Lam, Ben Shih, Tanay Mehta, Alex Mitev, and Zhiyang Ong. "Machine Learning Mechanisms for Quantum Robotics." In Quantum Robotics, 47–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-02520-4_5.

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Pastorello, Davide. "Quantum Classification." In Concise Guide to Quantum Machine Learning, 69–87. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6_7.

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Conference papers on the topic "Quantum machine"

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AHARONOV, YAKIR, Jeeva ANANDAN, and Lev VAIDMAN. "QUANTUM TIME MACHINE." In Proceedings of the International Conference on Fundamental Aspects of Quantum Theory — to Celebrate 30 Years of the Aharonov-Bohm-Effect. WORLD SCIENTIFIC, 1991. http://dx.doi.org/10.1142/9789814439251_0029.

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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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Isberg, Thomas A., and G. Michael Morris. "Invariant Pattern Recognition Using Quantum-Limited Images." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/mv.1987.thc3.

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In recent papers by Sheng and Arsenault1, and Wu and Stark2, rotation-, scale-and position-invariant image recognition was achieved by combining the rotation-invariant properties of the circular harmonic expansion with the scale-invariant properties of the Mellin transform. This combination was shown to provide a method for image recognition that is robust in the presence of noise, in addition to its desirable invariant properties. However, the computational complexity of these methods makes it is difficult to implement the recognition algorithms in real time.
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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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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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Malov, Dmitrii. "Quantum Algebraic Machine Learning." In 2020 IEEE 10th International Conference on Intelligent Systems (IS). IEEE, 2020. http://dx.doi.org/10.1109/is48319.2020.9199982.

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Perrier, Elija. "Quantum Fair Machine Learning." In AIES '21: AAAI/ACM Conference on AI, Ethics, and Society. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461702.3462611.

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Yong Liu, Junjie Wu, and Xun Yi. "Quantum Boson-Sampling Machine." In The 2015 11th International Conference on Natural Computation. IEEE, 2015. http://dx.doi.org/10.1109/icnc.2015.7378023.

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Satuluri, V. K. R. Rajeswari, and Vijayakumar Ponnusamy. "Quantum-Enhanced Machine Learning." In 2021 Smart Technologies, Communication and Robotics (STCR). IEEE, 2021. http://dx.doi.org/10.1109/stcr51658.2021.9589016.

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Reports on the topic "Quantum machine"

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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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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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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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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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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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Tamulis, Arvydas, and Jelena Tamuliene. Ab Initio Quantum Chemical Design of Single Supermolecule Photoactive Machines and Molecular Logical Devices. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada388289.

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Kirchhoff, Helmut, and Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, February 2014. http://dx.doi.org/10.32747/2014.7699861.bard.

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In this project, we studied the photosynthetic apparatus during dehydration and rehydration of the homoiochlorophyllous resurrection plant Craterostigmapumilum (retains most of the photosynthetic components during desiccation). Resurrection plants have the remarkable capability to withstand desiccation, being able to revive after prolonged severe water deficit in a few days upon rehydration. Homoiochlorophyllous resurrection plants are very efficient in protecting the photosynthetic machinery against damage by reactive oxygen production under drought. The main purpose of this BARD project was to unravel these largely unknown protection strategies for C. pumilum. In detail, the specific objectives were: (1) To determine the distribution and local organization of photosynthetic protein complexes and formation of inverted hexagonal phases within the thylakoid membranes at different dehydration/rehydration states. (2) To determine the 3D structure and characterize the geometry, topology, and mechanics of the thylakoid network at the different states. (3) Generation of molecular models for thylakoids at the different states and study the implications for diffusion within the thylakoid lumen. (4) Characterization of inter-system electron transport, quantum efficiencies, photosystem antenna sizes and distribution, NPQ, and photoinhibition at different hydration states. (5) Measuring the partition of photosynthetic reducing equivalents between the Calvin cycle, photorespiration, and the water-water cycle. At the beginning of the project, we decided to use C. pumilum instead of C. wilmsii because the former species was available from our collaborator Dr. Farrant. In addition to the original two dehydration states (40 relative water content=RWC and 5% RWC), we characterized a third state (15-20%) because some interesting changes occurs at this RWC. Furthermore, it was not possible to detect D1 protein levels by Western blot analysis because antibodies against other higher plants failed to detect D1 in C. pumilum. We developed growth conditions that allow reproducible generation of different dehydration and rehydration states for C. pumilum. Furthermore, advanced spectroscopy and microscopy for C. pumilum were established to obtain a detailed picture of structural and functional changes of the photosynthetic apparatus in different hydrated states. Main findings of our study are: 1. Anthocyan accumulation during desiccation alleviates the light pressure within the leaves (Fig. 1). 2. During desiccation, stomatal closure leads to drastic reductions in CO2 fixation and photorespiration. We could not identify alternative electron sinks as a solution to reduce ROS production. 3. On the supramolecular level, semicrystalline protein arrays were identified in thylakoid membranes in the desiccated state (see Fig. 3). On the electron transport level, a specific series of shut downs occur (summarized in Fig. 2). The main events include: Early shutdown of the ATPase activity, cessation of electron transport between cyt. bf complex and PSI (can reduce ROS formation at PSI); at higher dehydration levels uncoupling of LHCII from PSII and cessation of electron flow from PSII accompanied by crystal formation. The later could severe as a swift PSII reservoir during rehydration. The specific order of events in the course of dehydration and rehydration discovered in this project is indicative for regulated structural transitions specifically realized in resurrection plants. This detailed knowledge can serve as an interesting starting point for rationale genetic engineering of drought-tolerant crops.
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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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