Готові списки джерел за темами / Quantum Machine Learning (QML)

Добірка наукової літератури з теми "Quantum Machine Learning (QML)"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Quantum Machine Learning (QML)"

1

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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Анотація:
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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2

Karandashev, Konstantin, and O. Anatole von Lilienfeld. "An orbital-based representation for accurate quantum machine learning." Journal of Chemical Physics 156, no. 11 (March 21, 2022): 114101. http://dx.doi.org/10.1063/5.0083301.

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Анотація:
We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio calculations and explicitly accounts for changes in the electronic structure. We demonstrate the accuracy and flexibility of resulting QML models when applied to property labels, such as total potential energy, HOMO and LUMO energies, ionization potential, and electron affinity, using as datasets for training and testing entries from the QM7b, QM7b-T, QM9, and LIBE libraries. For the latter, we also demonstrate the ability of this approach to account for molecular species of different charge and spin multiplicity, resulting in QML models that infer total potential energies based on geometry, charge, and spin as input.
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3

Choppakatla, Arathi. "Quantum Machine Learning: Bridging the Gap Between Quantum Computing and Artificial Intelligence: An Overview." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 1149–53. http://dx.doi.org/10.22214/ijraset.2023.55318.

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Анотація:
Abstract: Quantum Machine Learning (QML) at the intersection of quantum computing and artificial intelligence (AI) is explored, emphasizing its role in connecting these domains. The transformative potential of QML in enhancing classical machine learning and the introduction of the Variational Quantum Classifier (VQC) algorithm (Ref. 4) are highlighted. Fundamental quantum principles, quantum feature maps, and the VQC's use of parameterized quantum circuits are discussed (Refs. 1, 3). The paper addresses practical implementation, optimization techniques, and the VQC's performance through empirical evaluations (Ref. 4). Implications of QML extend to diverse applications (Ref. 5), positioning it as a bridge between quantum computing and AI to unlock transformative possibilities.
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4

Avramouli, Maria, Ilias Κ. Savvas, Anna Vasilaki, and Georgia Garani. "Unlocking the Potential of Quantum Machine Learning to Advance Drug Discovery." Electronics 12, no. 11 (May 25, 2023): 2402. http://dx.doi.org/10.3390/electronics12112402.

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Анотація:
The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of machine learning (ML), leading to the emergence of quantum machine learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals to the classical ones, and a hybrid solution is the recommended approach at present.
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5

T West, Maxwell, Martin Sevior, and Muhammad Usman. "Reflection equivariant quantum neural networks for enhanced image classification." Machine Learning: Science and Technology 4, no. 3 (August 24, 2023): 035027. http://dx.doi.org/10.1088/2632-2153/acf096.

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Анотація:
Abstract Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.
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6

Christensen, Anders S., and O. Anatole von Lilienfeld. "Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.

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Анотація:
The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.
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7

Nguyen, Quoc Chuong, Le Bin Ho, Lan Nguyen Tran, and Hung Q. Nguyen. "Qsun: an open-source platform towards practical quantum machine learning applications." Machine Learning: Science and Technology 3, no. 1 (March 1, 2022): 015034. http://dx.doi.org/10.1088/2632-2153/ac5997.

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Анотація:
Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.
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8

Srikumar, Maiyuren, Charles D. Hill, and Lloyd C. L. Hollenberg. "Clustering and enhanced classification using a hybrid quantum autoencoder." Quantum Science and Technology 7, no. 1 (December 21, 2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

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Анотація:
Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.
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9

Kumar, Tarun, Dilip Kumar, and Gurmohan Singh. "Performance Analysis of Quantum Classifier on Benchmarking Datasets." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 375–80. http://dx.doi.org/10.37391/ijeer.100252.

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Анотація:
Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.
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10

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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Більше джерел

Дисертації з теми "Quantum Machine Learning (QML)"

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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Анотація:
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

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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Анотація:
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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3

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.

Повний текст джерела
Анотація:
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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4

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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Анотація:
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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5

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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6

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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Анотація:
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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7

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

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Анотація:
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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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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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

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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10

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

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Decoherence remains a major challenge in Near term, Intermediate scale Noisy Quantum (NISQ) computers. This thesis proposes techniques for characterizing classical noise correlations and performance variations in NISQ devices using single-qubit projective measurements. The central tasks of qubit state estimation and prediction are considered for measurements that are either temporally or spatially correlated. Firstly, this thesis focuses on timeseries prediction for single-qubit measurement outcomes. Focusing on repeated Ramsey measurements performed on a single qubit subject to temporally correlated dephasing, the key challenge is to predict qubit state dynamics by learning temporal noise correlations in a real-time stream of incoming measurements. Autoregressive or Fourier-based Kalman Filtering (KF) protocols are investigated for maximizing the forward prediction horizon and autoregressive approaches demonstrate superior predictive capabilities. Secondly, this thesis investigates the utility of nonlinear stochastic methods to characterize quantum systems using spatially correlated single-qubit projective measurements, via particle filters. A novel likelihood function is presented and incorporated into conventional particle filters. An adaptive procedure, `NMQA', is proposed to characterize a spatially inhomogeneous dephasing field in 2D by allocating measurements on a multi-qubit array. NMQA outperforms brute-force mapping in simulations and using experimental data. Finally, spatial prediction in 2D is compared with bivariate interpolation on a geometric arrangement of points known as the Padua points. By measuring a spatially inhomogeneous dephasing field on dedicated `sensor qubits', the objective is to predict the value of the field on unmeasured, proximal `data qubits'. The number and arrangement of sensor qubits relative to a fixed lattice of data qubits is investigated, providing insights for a general purpose predictive mapping protocol.
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Книги з теми "Quantum Machine Learning (QML)"

1

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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2

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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3

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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4

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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5

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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6

Subramanian, Thiruselvan, Archana Dhyani, Adarsh Kumar, and Sukhpal Singh Gill. Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003250357.

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7

Ran, Shi-Ju. Tensor Network Contractions: Methods and Applications to Quantum Many-Body Systems. Cham: Springer Nature, 2020.

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8

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Sourav De, Elizabeth Behrman, and Susanta Chakraborti, eds. Quantum Machine Learning. De Gruyter, 2020. http://dx.doi.org/10.1515/9783110670707.

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9

Quantum Machine Learning. Elsevier, 2014. http://dx.doi.org/10.1016/c2013-0-19170-2.

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10

Bhattacharyya, Siddhartha, Indrajit Pan, Ashish Mani, Elizabeth Behrman, and Susanta Chakraborti. Quantum Machine Learning. de Gruyter GmbH, Walter, 2020.

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Частини книг з теми "Quantum Machine Learning (QML)"

1

Pastorello, Davide. "QML Toolkit." In Concise Guide to Quantum Machine Learning, 49–56. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6897-6_5.

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2

Ganguly, Santanu. "QML Techniques." In Quantum Machine Learning: An Applied Approach, 317–402. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_7.

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3

Ganguly, Santanu. "QML Algorithms II." In Quantum Machine Learning: An Applied Approach, 277–315. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_6.

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4

Ganguly, Santanu. "QML: Way Forward." In Quantum Machine Learning: An Applied Approach, 461–96. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_9.

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5

Ganguly, Santanu. "QML Algorithms I." In Quantum Machine Learning: An Applied Approach, 205–76. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7098-1_5.

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6

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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7

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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8

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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9

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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Тези доповідей конференцій з теми "Quantum Machine Learning (QML)"

1

Dhotre, Saloni, Karan Doshi, Sneha Satish, and Kalpita Wagaskar. "Exploring Quantum Machine Learning (QML) for Earthquake Prediction." In 2022 International Conference on Intelligent Technologies (CONIT). IEEE, 2022. http://dx.doi.org/10.1109/conit55038.2022.9848250.

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2

Rehm, F., S. Vallecorsa, K. Borras, and D. Krücker. "QUANTUM MACHINE LEARNING FOR HEP DETECTOR SIMULATIONS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.62.94.001.

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Анотація:
Quantum Machine Learning (qML) is one of the most promising and very intuitive applications onnear-term quantum devices which possess the potential to combat computing resource challengesfaster than traditional computers. Classical Machine Learning (ML) is taking up a significant role inparticle physics to speed up detector simulations. Generative Adversarial Networks (GANs) haveproven to achieve a similar level of accuracy compared to Monte Carlo-based simulations whiledecreasing the computation time by orders of magnitude. In this research we are moving on and applyquantum computing to GAN-based detector simulations.Given the limitations of current quantum hardware in terms of number of qubits, connectivity, andnoise, we perform initial tests with a simplified GAN model running on quantum simulators. Themodel is a classical-quantum hybrid ansatz. It consists of a quantum generator, defined as aparameterised circuit based on single and two qubit gates, combined with a classical discriminator.Our initial qGAN prototype focuses on a one-dimensional toy-distribution, representing the energydeposited in a detector by a single particle. It employs three qubits and achieves high physics accuracythanks to hyper-parameter optimisation. Furthermore, we study the influence of real hardware noisefor the qML GAN training. A second qGAN is developed to simulate 2D images with a 64-pixelresolution, representing the energy deposition patterns in the detector. Different quantum ansatzes arestudied. We obtained the best results using a tree-tensor-network architecture with six qubits.Additionally, we discuss challenges and potential benefits of quantum computing as well as our plansfor future developments.
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3

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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4

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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5

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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6

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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7

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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8

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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9

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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Rückmann, Max, Sebastian Kleis, Christian G. Schaeffer, and Darko Zibar. "Machine Learning in Quantum Communication." In Signal Processing in Photonic Communications. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/sppcom.2020.sptu3i.6.

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Звіти організацій з теми "Quantum Machine Learning (QML)"

1

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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2

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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3

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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4

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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5

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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6

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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