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Journal articles on the topic 'Quantum Machine Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Patel, Ananya (Ph D. Candidate). "ADVANCING FINANCIAL PREDICTION THROUGH QUANTUM MACHINE LEARNING." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 1–7. https://doi.org/10.55640/ijidml-v02i02-01.

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The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application

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2

Peleshenko, Vitaly A. "QUANTUM MACHINE LEARNING." SOFT MEASUREMENTS AND COMPUTING 11, no. 60 (2022): 82–107. http://dx.doi.org/10.36871/2618-9976.2022.11.008.

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Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discu

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Shaik, Riyaaz Uddien, Aiswarya Unni, and Weiping Zeng. "Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers." Remote Sensing 14, no. 22 (2022): 5774. http://dx.doi.org/10.3390/rs14225774.

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A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for

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5

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

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6

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

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

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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 federa

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Sarah, L. Johnson. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 31–41. https://doi.org/10.5281/zenodo.15147384.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behi

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Gaurav, Kashyap. "Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–7. https://doi.org/10.5281/zenodo.14615549.

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At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector m

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Pudenz, Kristen L., and Daniel A. Lidar. "Quantum adiabatic machine learning." Quantum Information Processing 12, no. 5 (2012): 2027–70. http://dx.doi.org/10.1007/s11128-012-0506-4.

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Saini, Shivani, PK Khosla, Manjit Kaur, and Gurmohan Singh. "Quantum Driven Machine Learning." International Journal of Theoretical Physics 59, no. 12 (2020): 4013–24. http://dx.doi.org/10.1007/s10773-020-04656-1.

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Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.

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Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design,

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13

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

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum c

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Wichert, Andreas. "Quantum Machine Learning—Quo Vadis?" Entropy 26, no. 11 (2024): 905. http://dx.doi.org/10.3390/e26110905.

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The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the possibility of compressed data representation. However, can we really apply quantum machine learning for real-world applications? What are the advantages of quantum machine learning algorithms in addition to some proposed artificial problems? Is the promised exponential or quadratic speed-up realistic, assumi

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Tychola, Kyriaki A., Theofanis Kalampokas, and George A. Papakostas. "Quantum Machine Learning—An Overview." Electronics 12, no. 11 (2023): 2379. http://dx.doi.org/10.3390/electronics12112379.

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Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we con

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Jerbi, Sofiene, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, and Vedran Dunjko. "Shadows of quantum machine learning." Nature Communications 15 (July 6, 2024): 5676. https://doi.org/10.1038/s41467-024-49877-8.

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Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a major obstacle to the widespread use of quantum machine learningmodels in practice is that these models, even once trained, still require access to a quantumcomputer in order to be evaluated on new data. To solve this issue, we introduce a class of quantum models where quantum resources are only required during training, while the deployment of the trained model is classical. Specifically, the training phase of our mod

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Kaur Sidhu, Brahmaleen. "Unlocking the Power of Quantum Mechanics for Machine Learning." International Journal of Science and Research (IJSR) 11, no. 3 (2022): 1574–82. http://dx.doi.org/10.21275/sr24427201406.

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18

Li, Keqin, Peng Zhao, Shuying Dai, et al. "Exploring the Impact of Quantum Computing on Machine Learning Performance." Middle East Journal of Applied Science & Technology 07, no. 02 (2024): 145–61. http://dx.doi.org/10.46431/mejast.2024.7215.

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This paper delves into the integration of machine learning and quantum computing, highlighting the potential of quantum computing to enhance the performance and computational efficiency of machine learning. Through theoretical analysis and experimental studies, this paper demonstrates how quantum computing can accelerate traditional machine learning algorithms via its unique properties of superposition and entanglement, particularly in handling large datasets and solving high-dimensional problems. Detailed introductions to quantum-enhanced machine learning models such as quantum neural network

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Roggero, Alessandro, Jakub Filipek, Shih-Chieh Hsu, and Nathan Wiebe. "Quantum Machine Learning with SQUID." Quantum 6 (May 30, 2022): 727. http://dx.doi.org/10.22331/q-2022-05-30-727.

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In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimiza

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Melo, André, Nathan Earnest-Noble, and Francesco Tacchino. "Pulse-efficient quantum machine learning." Quantum 7 (October 9, 2023): 1130. http://dx.doi.org/10.22331/q-2023-10-09-1130.

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Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level. A recent addition to this toolbox of techniques is pulse-efficient transpilation, which reduces circuit schedule duration by expl

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Lohia, Aarav. "Quantum Artificial Intelligence: Enhancing Machine Learning with Quantum Computing." Journal of Quantum Science and Technology 1, no. 2 (2024): 6–11. http://dx.doi.org/10.36676/jqst.v1.i2.9.

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Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data mor

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22

Pulicharla, Mohan Raja. "Hybrid Quantum-Classical Machine Learning Models: Powering the Future of AI." Journal of Science & Technology 4, no. 1 (2023): 40–65. http://dx.doi.org/10.55662/jst.2023.4102.

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The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial

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23

WANG Peng and MAIMAITINIYAZI Maimaitiabudula. "Quantum Dynamics of Machine Learning." Acta Physica Sinica 74, no. 6 (2025): 0. https://doi.org/10.7498/aps.74.20240999.

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To address the current lack of rigorous theoretical models in the machine learning process, this paper adopts the quantum dynamic method to model the iterative motion process of machine learning based on the principles of first-principles thinking. This approach treats the iterative evolution of algorithms as a physical motion process, defines a generalized objective function in the parameter space of machine learning algorithms, and views the iterative process of machine learning as the process of seeking the optimal value for this generalized objective function. In physical terms, this proce

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24

Gugri, Apoorva. "Hate-Speech Recognition using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41180.

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The convergence of quantum computing and genomic data analysis offers a groundbreaking methodology for tackling intricate biological datasets. Traditional computational techniques often struggle with the complexities of high-dimensional genomic information. This study introduces a quantum-enhanced framework for cancer type detection, leveraging the capabilities of Quantum Support Vector Machines (QSVM). The proposed approach showcases significant advancements in efficiency, particularly in training speed and scalability, highlighting the potential of quantum algorithms as valuable extensions t

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Pushkar, Mehendale. "Quantum Machine Learning: The Next Frontier in AI." Journal of Scientific and Engineering Research 10, no. 1 (2023): 104–8. https://doi.org/10.5281/zenodo.13753380.

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Quantum Machine Learning (QML) stands at the intersection of two groundbreaking fields: quantum computing and artificial intelligence. This paper explores the potential of QML to revolutionize AI by leveraging the unique capabilities of quantum mechanics. It delves into the principles of quantum computing, the integration of quantum algorithms with machine learning, and the emerging applications that highlight the transformative power of QML. The paper also discusses the challenges and ethical considerations associated with this nascent field, aiming to provide a comprehensive overview of QML

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26

Khan, Muhammad Jawad, Sumeera Bibi, Muzammil Ahmad Khan, et al. "Investigating Quantum Machine Learning Frameworks and Simulating Quantum Approaches." Asian Bulletin of Big Data Management 4, no. 4 (2024): 34–43. http://dx.doi.org/10.62019/abbdm.v4i4.232.

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Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, ex

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Pramoda Medisetty. "Quantum Machine Learning: A Survey." Journal of Electrical Systems 20, no. 6s (2024): 971–81. http://dx.doi.org/10.52783/jes.2778.

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Quantum Machine Learning (QML) is an emergent discipline that integrates the principles of quantum computing with traditional machine learning techniques, aiming to enhance the capabilities of data analysis and decision-making processes. Leveraging the unique properties, QML promises to revolutionize machine learning by offering superior processing power and computational efficiency. The synergistic approach followed by each Quantum Machine Learning Algorithm allows for the management of large databases and the execution of complex computational tasks more efficiently than classical algorithms

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Fung, Fred. "QUANTUM SOFTWARE: Quantum Machine Learning in Telecommunication." Digitale Welt 6, no. 2 (2022): 30–31. http://dx.doi.org/10.1007/s42354-022-0472-7.

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Cárdenas‐López, Francisco A., Mikel Sanz, Juan Carlos Retamal, and Enrique Solano. "Enhanced Quantum Synchronization via Quantum Machine Learning." Advanced Quantum Technologies 2, no. 7-8 (2019): 1800076. http://dx.doi.org/10.1002/qute.201800076.

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Lamata, Lucas, Mikel Sanz, and Enrique Solano. "Quantum Machine Learning and Bioinspired Quantum Technologies." Advanced Quantum Technologies 2, no. 7-8 (2019): 1900075. http://dx.doi.org/10.1002/qute.201900075.

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31

Schetakis, Nikolaos, Davit Aghamalyan, Michael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, and Paul Robert Griffin. "Quantum Machine Learning for Credit Scoring." Mathematics 12, no. 9 (2024): 1391. http://dx.doi.org/10.3390/math12091391.

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This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset

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Fabrizio, Alberto, Benjamin Meyer, Raimon Fabregat, and Clemence Corminboeuf. "Quantum Chemistry Meets Machine Learning." CHIMIA International Journal for Chemistry 73, no. 12 (2019): 983–89. http://dx.doi.org/10.2533/chimia.2019.983.

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In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

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Gonaygunta, Hari, Mohan Harish Maturi, Geeta Sandeep Nadella, Karthik Meduri, and Snehal Satish. "Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning Models." International Journal of Advanced Engineering Research and Science 11, no. 5 (2024): 35–41. http://dx.doi.org/10.22161/ijaers.115.5.

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Using quantum algorithms to improve deep learning models' capabilities is becoming increasingly popular as quantum computing develops. In this work, we investigate how quantum algorithms using quantum neural networks (QNNs) might enhance the effectiveness and performance of deep learning models. We examine the effects of quantum-inspired methods on tasks, including regression, sorting, and optimization, by thoroughly analyzing quantum algorithms and how they integrate with deep learning systems. We experiment with Estimator QNN and Sampler QNN implementations using Qiskit machine-learning, ana

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Dr., Sudha.J, Anuradha.V Anbarasi.S, Banu.H Falila, and Mounika.R. "Storm Chasing Using Quantum Machine Learning." International Journal of Multidisciplinary Research Transactions 5, no. 6 (2023): 151–59. https://doi.org/10.5281/zenodo.7883219.

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A vast amount of images generated from high resolution telescopes, satellites, on the ground cameras and other sources are processed daily to get various insights affecting economy, business, individual lives. This necessitates ability to store huge amount of data. During image processing different techniques are involved future recognition ,classification problems which belongs to supervised learning. This may not be provided using classical computation. Here quantum computing can be useful due to its ability to process huge amount of data. This Project aims to leverage current Noisy Intermed

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Spagnolo, Nicolò, Alessandro Lumino, Emanuele Polino, Adil S. Rab, Nathan Wiebe, and Fabio Sciarrino. "Machine Learning for Quantum Metrology." Proceedings 12, no. 1 (2019): 28. http://dx.doi.org/10.3390/proceedings2019012028.

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Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach such bound when only a small number of probes is employed. Here, we discuss an asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limite

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BANG, Jeongho. "Machine Learning and Quantum Algorithm." Physics and High Technology 26, no. 12 (2017): 25–29. http://dx.doi.org/10.3938/phit.26.048.

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Wang, Bingjie. "Quantum algorithms for machine learning." XRDS: Crossroads, The ACM Magazine for Students 23, no. 1 (2016): 20–24. http://dx.doi.org/10.1145/2983535.

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Carrasquilla, Juan. "Machine learning for quantum matter." Advances in Physics: X 5, no. 1 (2020): 1797528. http://dx.doi.org/10.1080/23746149.2020.1797528.

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Das Sarma, Sankar, Dong-Ling Deng, and Lu-Ming Duan. "Machine learning meets quantum physics." Physics Today 72, no. 3 (2019): 48–54. http://dx.doi.org/10.1063/pt.3.4164.

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Khan, Tariq M., and Antonio Robles-Kelly. "Machine Learning: Quantum vs Classical." IEEE Access 8 (2020): 219275–94. http://dx.doi.org/10.1109/access.2020.3041719.

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Stajic, Jelena. "Machine learning and quantum physics." Science 355, no. 6325 (2017): 591.15–593. http://dx.doi.org/10.1126/science.355.6325.591-o.

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Schuld, Maria. "Machine learning in quantum spaces." Nature 567, no. 7747 (2019): 179–81. http://dx.doi.org/10.1038/d41586-019-00771-0.

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Sheng, Yu-Bo, and Lan Zhou. "Distributed secure quantum machine learning." Science Bulletin 62, no. 14 (2017): 1025–29. http://dx.doi.org/10.1016/j.scib.2017.06.007.

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Hush, Michael R. "Machine learning for quantum physics." Science 355, no. 6325 (2017): 580. http://dx.doi.org/10.1126/science.aam6564.

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Nivelkar, Mukta, and S. G. Bhirud. "Modeling of Supervised Machine Learning using Mechanism of Quantum Computing." Journal of Physics: Conference Series 2161, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2161/1/012023.

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Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models

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Ciliberto, Carlo, Mark Herbster, Alessandro Davide Ialongo, et al. "Quantum machine learning: a classical perspective." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2209 (2018): 20170551. http://dx.doi.org/10.1098/rspa.2017.0551.

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Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mix

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Akrom, Muhamad. "Quantum Support Vector Machine for Classification Task: A Review." Journal of Multiscale Materials Informatics 1, no. 2 (2024): 1–8. http://dx.doi.org/10.62411/jimat.v1i2.10965.

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Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines,

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Bishwas, Arit Kumar, Ashish Mani, and Vasile Palade. "Gaussian kernel in quantum learning." International Journal of Quantum Information 18, no. 03 (2020): 2050006. http://dx.doi.org/10.1142/s0219749920500069.

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The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity

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Jaroszewski, Daniel, Benedikt Sturm, Wolfgang Mergenthaler, et al. "Supervised Learning Using Quantum Technology." PHM Society European Conference 5, no. 1 (2020): 7. http://dx.doi.org/10.36001/phme.2020.v5i1.1275.

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In this paper, we present classical machine learning algorithms enhanced by quantum technology to classify a data set. The data set contains binary input variables and binary output variables. The goal is to extend classical approaches such as neural networks by using quantum machine learning principles. Classical algorithms struggle as the dimensionality of the feature space increases. We examine the usage of quantum technologies to speed up these classical algorithms and to introduce the new quantum paradigm into machine diagnostic domain. Most of the prognosis models based on binary or mult

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

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

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