Relevant bibliographies by topics / Data structure for quantum machine learning

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data structure for quantum machine learning'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Data structure for quantum machine learning"

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Zhao, Zhikuan, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, and Joseph F. Fitzsimons. "Smooth input preparation for quantum and quantum-inspired machine learning." Quantum Machine Intelligence 3 (April 26, 2021): 14. https://doi.org/10.1007/s42484-021-00045-x.

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Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoot
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Schuhmacher, Julian, Guglielmo Mazzola, Francesco Tacchino, et al. "Extending the reach of quantum computing for materials science with machine learning potentials." AIP Advances 12, no. 11 (2022): 115321. http://dx.doi.org/10.1063/5.0099469.

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Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming classical counterparts on selected problem instances using limited quantum resources. These methods are still expected to feature a runtime preventing quantum simulations of large scale and bulk systems. In this work, we propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potenti
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Peters, Evan, and Maria Schuld. "Generalization despite overfitting in quantum machine learning models." Quantum 7 (December 20, 2023): 1210. http://dx.doi.org/10.22331/q-2023-12-20-1210.

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The widespread success of deep neural networks has revealed a surprise in classical machine learning: very complex models often generalize well while simultaneously overfitting training data. This phenomenon of benign overfitting has been studied for a variety of classical models with the goal of better understanding the mechanisms behind deep learning. Characterizing the phenomenon in the context of quantum machine learning might similarly improve our understanding of the relationship between overfitting, overparameterization, and generalization. In this work, we provide a characterization of
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Balakumar, Arvind. "Quantum K-means Clustering and Classical k Means Clustering For Chest Pain Classification Using Qiskit." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 945–48. http://dx.doi.org/10.22214/ijraset.2022.47484.

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Abstract: Quantum computing is an upcoming field of technology which has a broad scope of increasing the current technology in a robust manner. The application area Quantum computing is very huge which ranges from battery research, protein structure research to advance computing and security areas like cryptography, quantum internet, quantum machine learning and quantum cyber security. The quantum machine learning area seems to be the most interesting because of the computing capability of a real time quantum computer. With the quantum machine learning algorithm, classical algorithm, processin
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Chakraborty, Sanjay, Soharab Hossain Shaikh, Sudhindu Bikash Mandal, Ranjan Ghosh, and Amlan Chakrabarti. "A study and analysis of a discrete quantum walk-based hybrid clustering approach using d-regular bipartite graph and 1D lattice." International Journal of Quantum Information 17, no. 02 (2019): 1950016. http://dx.doi.org/10.1142/s0219749919500163.

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Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid quantum algorithm for data clustering (quantum walk-based hybrid clustering (QWBHC)) is introduced where one-dimensional discrete time quantum walks (DTQW) play the central role to update the positions of data points according to their probability distr
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Ozpolat, Zeynep, and Murat Karabatak. "Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification." Diagnostics 13, no. 6 (2023): 1099. http://dx.doi.org/10.3390/diagnostics13061099.

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The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods
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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 (2019): 1028–31. http://dx.doi.org/10.2533/chimia.2019.1028.

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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
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Zuev, S. V. "Geometric properties of quantum entanglement and machine learning." Russian Technological Journal 11, no. 5 (2023): 19–33. http://dx.doi.org/10.32362/2500-316x-2023-11-5-19-33.

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Objectives. Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. This paper aims to propose and verify a method of such analysis based on the representation of data in the form of a quantum state, or, alternatively, in the form of a geometric object in a space allowing online machine learning.Methods. This paper uses Feynman formalism to represent quantum states and operations on them, the representation of quantum computing in the form of quantum circuits, geometric transformations, topological classification, as well
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Convy, Ian, William Huggins, Haoran Liao, and K. Birgitta Whaley. "Mutual information scaling for tensor network machine learning." Machine Learning: Science and Technology 3, no. 1 (2022): 015017. http://dx.doi.org/10.1088/2632-2153/ac44a9.

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Abstract Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those f
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Raubitzek, Sebastian, and Kevin Mallinger. "On the Applicability of Quantum Machine Learning." Entropy 25, no. 7 (2023): 992. http://dx.doi.org/10.3390/e25070992.

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In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these classifiers when using a hyperparameter search on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations,
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Dissertations / Theses on the topic "Data structure for quantum machine learning"

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ERBA, VITTORIO. "Aspects of data structure in machine learning." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/873262.

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It is widely believed that understanding data structure is a crucial ingredient to push forward our comprehension on how (and why) modern machine learning works. Still, most of the theoretical results we have are obtained under very simplifying assumptions on the structure of the training data. In this Thesis, I review some novel results on the problem of characterizing the geometric structure of datasets and the consequences that this structure has on learning algorithms. I also provide pedagogical introductions to manifold learning, random geometric graphs theory and supervised binary c
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ERBA, VITTORIO. "ASPECTS OF DATA STRUCTURE IN MACHINE LEARNING." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/873502.

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It is widely believed that understanding data structure is a crucial ingredient to push forward our comprehension on how (and why) modern machine learning works. Still, most of the theoretical results we have are obtained under very simplifying assumptions on the structure of the training data. In this Thesis, I review some novel results on the problem of characterizing the geometric structure of datasets and the consequences that this structure has on learning algorithms. I also provide pedagogical introductions to manifold learning, random geometric graphs theory and supervised binary c
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Lever, G. "Exploiting structure defined by data in machine learning : some new analyses." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1302070/.

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This thesis offers some new analyses and presents some new methods for learning in the context of exploiting structure defined by data – for example, when a data distribution has a submanifold support, exhibits cluster structure or exists as an object such as a graph. 1. We present a new PAC-Bayes analysis of learning in this context, which is sharp and in some ways presents a better solution than uniform convergence methods. The PAC-Bayes prior over a hypothesis class is defined in terms of the unknown true risk and smoothness of hypotheses w.r.t. the unknown data-generating distribution. The
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Hu, Hae-Jin. "Design of Comprehensible Learning Machine Systems for Protein Structure Prediction." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/22.

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With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing cla
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Straub, Kayla Marie. "Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71320.

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Email correspondence has become the predominant method of communication for businesses. If not for the inherent privacy concerns, this electronically searchable data could be used to better understand how employees interact. After the Enron dataset was made available, researchers were able to provide great insight into employee behaviors based on the available data despite the many challenges with that dataset. The work in this thesis demonstrates a suite of methods to an appropriately anonymized academic email dataset created from volunteers' email metadata. This new dataset, from an int
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Polianskii, Vladislav. "An Investigation of Neural Network Structure with Topological Data Analysis." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238702.

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Artificial neural networks at the present time gain notable popularity and show astounding results in many machine learning tasks. This, however, also results in a drawback that the understanding of the processes happening inside of learning algorithms decreases. In many cases, the process of choosing a neural network architecture for a problem comes down to selection of network layers by intuition and to manual tuning of network parameters. Therefore, it is important to build a strong theoretical base in this area, both to try to reduce the amount of manual work in the future and to get a bet
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Abu-Hakmeh, Khaldoon Emad. "Assessing the use of voting methods to improve Bayesian network structure learning." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45826.

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Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning
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Iqbal, Sumaiya. "Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2379.

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Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs
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Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.

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Todorov, Helena. "Structure learning to unravel mechanisms of the immune system." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN084.

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Les cellules de notre système immunitaire jouent un rôle essentiel en nous protégeant de pathogènes infectieux tels que les virus ou certaines bactéries. Lors d’une maladie, les différents types de cellules immunitaires jouent des rôles spécifiques et interagissent, générant ainsi une réponse immunitaire adéquate. Cependant, cette réponse immunitaire complexe peut parfois être perturbée. Par exemple, les cellules qui sont supposées combattre l’infection peuvent être rendues silencieuses. Ce phénomène est observé dans certaines tumeurs, dans lesquelles des cellules peuvent commencer à prolifére
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Books on the topic "Data structure for quantum machine learning"

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Michael, Affenzeller, ed. Genetic algorithms and genetic programming: Modern concepts and practical applications. Chapman & Hall/CRC, 2009.

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Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2016.

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Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

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Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

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Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

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Physics of Data Science and Machine Learning. CRC Press, 2021.

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Smith, Noah A. Linguistic Structure Prediction. Springer International Publishing AG, 2011.

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Linguistic structure prediction. Morgan & Claypool, 2011.

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Book chapters on the topic "Data structure for quantum machine learning"

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Guan, Ji, Wang Fang, and Mingsheng Ying. "Verifying Fairness in Quantum Machine Learning." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_20.

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AbstractDue to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition—any two similar individuals must be treated similarly and are thus unbiased. We show that quantum noise can improve fairness and develop an algorithm to check whether a (noisy) quantum machine learning model is fair. In particular, this algorithm can find bias kernels of quantum data (encoding individuals) during checking. These bias kernels generate infinitely many bias pairs for investigating the unfairness of the model. Our algorithm is designed based on a highly efficient data structure—Tensor Networks—and implemented on Google’s TensorFlow Quantum. The utility and effectiveness of our algorithm are confirmed by the experimental results, including income prediction and credit scoring on real-world data, for a class of random (noisy) quantum decision models with 27 qubits ($$2^{27}$$ 2 27 -dimensional state space) tripling ($$2^{18}$$ 2 18 times more than) that of the state-of-the-art algorithms for verifying quantum machine learning models.
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Tanaka, Akihiro, Juniper Tyree, Anton Björklund, Jarmo Mäkelä, and Kai Puolamäki. "$$\chi $$iplot: Web-First Visualisation Platform for Multidimensional Data." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43430-3_26.

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Abstract$$\chi $$iplot is an HTML5-based system for interactive exploration of data and machine learning models. A key aspect is interaction, not only for the interactive plots but also between plots. Even though $$\chi $$iplot is not restricted to any single application domain, we have developed and tested it with domain experts in quantum chemistry to study molecular interactions and regression models. $$\chi $$iplot can be run both locally and online in a web browser (keeping the data local). The plots and data can also easily be exported and shared. A modular structure also makes $$\chi $$iplot optimal for developing machine learning and new interaction methods.
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Schuld, Maria, and Francesco Petruccione. "Quantum Machine Learning." In Encyclopedia of Machine Learning and Data Science. Springer US, 2023. http://dx.doi.org/10.1007/978-1-4899-7502-7_913-2.

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

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Chakraborty, Sanjay, and Lopamudra Dey. "Quantum Computing in Machine Learning." In Data-Intensive Research. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8004-6_7.

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McClarren, Ryan G. "Finding Structure Within a Data Set: Data Reduction and Clustering." In Machine Learning for Engineers. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70388-2_4.

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Pinilla, Jose P., and Steven J. E. Wilton. "Structure-Aware Minor-Embedding for Machine Learning in Quantum Annealing Processors." In Quantum Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37966-6_5.

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Klopotek, Mieczyslaw A. "Learning belief network structure from data under causal insufficiency." In Machine Learning: ECML-94. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_78.

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Rauf, Ijaz A. "An Overview of Quantum Mechanics." In Physics of Data Science and Machine Learning. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206743-3.

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Conference papers on the topic "Data structure for quantum machine learning"

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Shrivastava, Aviral, and Vinayak Sharma. "Primer on Data in Quantum Machine Learning." In 2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES). IEEE, 2024. http://dx.doi.org/10.1109/cases60062.2024.00010.

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Ramos-Pulido, Sofía, Neil Hernández-Gress, Glen S. Uehara, Andreas Spanias, and Héctor Ceballos-Cancino. "Implementation of Quantum Machine Learning on Educational Data." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013154500003890.

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Islam, Md Majedul, and Jing Selena He. "Abdominal Trauma Detection using Hybrid Quantum Machine Learning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825833.

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Dhanalakshmi, K., and G. Nagarajan. "Quantum Machine Learning for Invasive Ductal Carcinoma Classification using Quantum Kernels." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810956.

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Bagoun, Yassine, Ahmed Zinedine, and Ismail Berrada. "Autism Spectrum Disorder Detection with Quantum Machine Learning Methods." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756377.

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Chester, Mason, Ethan Barton, Andrew Liban, Andrew Polisetty, and Yong Shi. "Quantum-Based Multi-Model Machine Learning for Security Data Analysis." In 2024 IEEE 4th International Conference on Advanced Learning Technologies on Education & Research (ICALTER). IEEE, 2024. https://doi.org/10.1109/icalter65499.2024.10819207.

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Thelasingha, Neelanga, and Thilanka Munasinghe. "Energy Infrastructure Risk Modeling using Quantum and Classical Machine Learning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825420.

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Hernandez, Andres Correa, and Claire F. Gmachl. "Machine Learning for Quantum Cascade Laser Design and Optimization." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sw3h.3.

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A machine learning framework is used to predict the laser performance of 109 quantum cascade laser designs in 8 hours. The algorithm demonstrates how to optimize the layer structure, yielding a 2-fold increase in performance.
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Chen, Kuan-Cheng, Yi-Tien Li, Tai-Yu Li, Chen-Yu Liu, Po-Heng Henry Lee, and Cheng-Yu Chen. "CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data." In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2025. https://doi.org/10.1109/icasspw65056.2025.11011218.

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Braniff, Austin, Fengqi You, and Yuhe Tian. "Enhanced Reinforcement Learning-driven Process Design via Quantum Machine Learning." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.149501.

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In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning
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Reports on the topic "Data structure for quantum machine learning"

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Perdigão, Rui A. P., and Julia Hall. Augmented Post-Quantum Synergistic Manifold Intelligence for Complex System Dynamics and Coevolutionary Multi-Hazards. Synergistic Manifolds, 2024. https://doi.org/10.46337/241211.

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This work seeks to unveil system dynamic predictability beyond existing data and model horizons, and beyond methodological paradigms in classical physics, machine learning and artificial intelligence, in order to increase awareness and preparedness to predict and tackle complexity and multi-hazards in a changing world, one where the coevolution among humans and nature has been reshaping the structure and functioning of our planet and the multiscale multidomain interactions within. For this purpose, we design and implement our next-generation Information Physical suite of Augmented Post-Quantum
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Perdigão, Rui A. P., and Julia Hall. Empowering Next-Generation Synergies among Models and Data with Information Physical Quantum Technological Intelligence. Synergistic Manifolds, 2024. https://doi.org/10.46337/241209.

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We present and deploy our methodological and technological framework of Information Physical Quantum Technological Intelligence (IPQuTI), to empower next-generation mathematically robust, physically consistent, computationally efficient and operationally scalable synergies among models and data across multisectoral theoretical and applied workflows. Going beyond digital computing platforms, IPQuTI encompasses a richer basis alphabet of fundamental quantum states (information building blocks) and a high-order set of superposition and entanglement functionals (grammar) beyond the state of the ar
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Dutta, Sourav, Anna Wagner, Theadora Hall, and Nawa Raj Pradhan. Data-driven modeling of groundwater level using machine learning. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48452.

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This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at
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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar
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6

Peles, Amra, Scott Whalen, and Glenn Grant. Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1985698.

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Banco de España, 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic lite
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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Crowe. PR-261-15609-R01 Machine Learning Algorithms for Smart Meter Diagnostics Part II (TR2701). Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010862.

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Modern smart meters provide an abundance of diagnostic data. Detecting abnormalities in this data can be difficult given the sheer quantity of information. Determining what kind of readings constitute normal operation versus an impending problem has been the subject of significant research; however, there is still room for improvement in real-time fault monitoring. Statistical models known as Machine Learning Algorithms (MLAs) have been identified as a potential solution. A new feature set was selected that allowed for extension of MLAs to ultrasonic meters with different path arrangements. Pr
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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