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Artykuły w czasopismach na temat „Quantum Machine Learning (QML)”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 25 lipca 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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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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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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Karandashev, Konstantin, and O. Anatole von Lilienfeld. "An orbital-based representation for accurate quantum machine learning." Journal of Chemical Physics 156, no. 11 (2022): 114101. http://dx.doi.org/10.1063/5.0083301.

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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

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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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Chavan, Shradha, and Preeti Mulay. "Define, refined and re-defined concepts of quantum machine learning : A review." Journal of Information and Optimization Sciences 45, no. 5 (2024): 1229–62. http://dx.doi.org/10.47974/jios-1320.

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Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so o

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Parveen Shaik, Dr Sajeeda. "Exploring the Landscape: A Systematic Review of Quantum Machine Learning and Its Diverse Applications." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 8, no. 1 (2020): 05–09. http://dx.doi.org/10.55083/irjeas.2020.v08i01003.

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Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery,

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Olaitan, Ololade Funke, Samuel Oluwabukunmi Ayeni, Adedapo Olosunde, et al. "Quantum Computing in Artificial Intelligence: a Review of Quantum Machine Learning Algorithms." Path of Science 11, no. 5 (2025): 7001. https://doi.org/10.22178/pos.117-25.

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Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Netwo

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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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Akrom, Muhamad, Wise Herowati, and De Rosal Ignatius Moses Setiadi. "A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification." Journal of Computing Theories and Applications 2, no. 3 (2025): 355–67. https://doi.org/10.62411/jcta.11779.

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This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in perfect classification, with all models attaining accuracy, precision, recall, and an F1-score of 1.00

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Ajibosin, Surajudeen Shina, and Deniz Cetinkaya. "Implementation and Performance Evaluation of Quantum Machine Learning Algorithms for Binary Classification." Software 3, no. 4 (2024): 498–513. http://dx.doi.org/10.3390/software3040024.

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In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QN

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Li, Luning, Xuchen Zhang, Zhicheng Cui, et al. "An Overview of Quantum Machine Learning Research in China." Applied Sciences 15, no. 5 (2025): 2555. https://doi.org/10.3390/app15052555.

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Quantum machine learning (QML) is an emerging discipline that combines quantum computing and machine learning and is able to exhibit exponential superiority over classical machine learning regarding computing speed on specific problems. This article provides a comprehensive review of the QML research in China. The QML development in China is presented in terms of research ideas and tasks, and the algorithms and application fields are sorted out. We have also highlighted some typical creative studies and illuminated their innovation points. Furthermore, the current challenges and future prospec

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Guntuka, Sunny. "Quantum Machine Learning: Bridging Quantum Computing and Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 1455–60. http://dx.doi.org/10.22214/ijraset.2024.64377.

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Abstract: This comprehensive article explores the burgeoning field of Quantum Machine Learning (QML), examining its foundational principles, key algorithms, and potential applications. We delve into the fundamentals of quantum computing, including qubits, quantum gates, and the challenges of quantum measurement and decoherence. The article provides an indepth analysis of Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), comparing them with their classical counterparts and highlighting their unique advantages and limitations. We investigate the promising applications of

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Ganapathy, Venkatasubramanian. "Quantum Machine Learning for Anomaly Detection in Cyber Security Audits." Shodh Sari-An International Multidisciplinary Journal 04, no. 01 (2025): 127–54. https://doi.org/10.59231/sari7784.

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Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this

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Bangar Raju Cherukuri. "Quantum machine learning: Transforming cloud-based AI solutions." International Journal of Science and Research Archive 1, no. 1 (2020): 110–22. https://doi.org/10.30574/ijsra.2020.1.1.0041.

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This study examines the feasibility of placing quantum computing technology into cloud ML systems to make QML far faster and more scalable. Quantum computers tackle standard ML performance challenges through their special traits, including superposition and entanglement. Implementing QML on cloud-based platforms unlocks the specific advantages of scalability and accessibility while providing the required flexibility. Cloud-based systems can better predict results with faster performance when they use quantum algorithms to process machine learning tasks. This research examines how QML connects

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Lokesh BS. "Secure Quantum Machine Learning via Quantum Cryptography: Theoretical Framework and Implementation Insights." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 1255–65. https://doi.org/10.52783/jisem.v10i49s.10122.

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As quantum machine learning (QML) continues to evolve, it promises unparalleled computational advantages in processing complex data. However, the rise of QML also introduces critical concerns regarding data security and privacy, particularly in sensitive domains such as healthcare, finance, and defense. Classical cryptographic methods fall short in addressing threats that arise in quantum communication and computation environments. To bridge this gap, this paper presents a hybrid framework that integrates quantum cryptography—specifically Quantum Key Distribution (QKD)—with QML pipelines, ensu

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Rosemary J and Adrishya Maria Abraham. "Quantum Machine Learning Techniques for Network Defense: Comparative Study of Quantum vs. Classical Approaches." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 12 (2024): 3799–802. https://doi.org/10.47392/irjaem.2024.0564.

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With the potential for quantum computing to completely transform cybersecurity, quantum machine learning is becoming a ground-breaking technology. Cyber-attacks have been successfully countered by traditional network defense systems, which mostly use conventional machine learning (ML) techniques. However, the growing complexity of assaults and the exponential expansion of network data reveal the shortcomings of traditional methods, especially with regard to speed and scalability. By utilizing quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Var

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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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Bhagwakar, Priya Pareshbhai, Chirag Suryakant Thaker, and Hetal A. Joshiara. "A Review of quantum algorithms for prediction of hazardous asteroids." Computing and Artificial Intelligence 2, no. 1 (2024): 1141. http://dx.doi.org/10.59400/cai.v2i1.1141.

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Quantum computing (QC) and quantum machine learning (QML), two emerging technologies, have the potential to completely change how we approach solving difficult problems in physics and astronomy, among other fields. Potentially Hazardous Asteroids (PHAs) can produce a variety of damaging phenomena that put biodiversity and human life at serious risk. Although PHAs have been identified through the use of machine learning (ML) techniques, the current approaches have reached a point of saturation, signaling the need for additional innovation. This paper provides an in-depth examination of various

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Lalit Mohan Trivedi, Et al. "Exploring Quantum Machine Learning Algorithms for Enhanced Data Analysis." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1995–2001. http://dx.doi.org/10.17762/ijritcc.v11i10.8813.

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The rapid increase of records generation throughout diverse domain names has necessitated the development of superior records analysis techniques. Quantum gadget mastering (QML) has emerged as a promising paradigm that harnesses the computational power of quantum systems to beautify information evaluation duties. This studies paper explores the software of quantum machine getting to know algorithms to cope with demanding situations in information analysis, highlighting their capacity for progressed performance and scalability. We provide an overview of key QML algorithms, talk their blessings,

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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 (2023): 1149–53. http://dx.doi.org/10.22214/ijraset.2023.55318.

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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 empiri

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Nammouchi, Amal, Andreas Kassler, and Andreas Theocharis. "Quantum Machine Learning in Climate Change and Sustainability: A Short Review." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 107–14. http://dx.doi.org/10.1609/aaaiss.v2i1.27657.

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Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising paradigm that harnesses the power of quantum computing to address complex problems in various domains including climate change and sustainability. In this work, we survey existing literature that applies quantum machine learning to solve climate change and sustainability-related problems. We review promising QML methodologies that have the potential to accelerate d

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Jayakumar, Dhivya, and Srividhya Selvaraj. "Revolutionizing Financial Services with Quantum Machine Learning Techniques." Semarak International Journal of Machine Learning 3, no. 1 (2025): 1–10. https://doi.org/10.37934/sijml.3.1.110a.

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Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks,

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Jayakumar, Dhivya, and Srividhya Selvaraj. "Revolutionizing Financial Services with Quantum Machine Learning Techniques." Semarak International Journal of Machine Learning 3, no. 1 (2024): 1–10. https://doi.org/10.37934/sijml.3.1.110.

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Modern quantum machine learning algorithms and techniques are reviewed in this review paper with possible financial applications. Along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs), we discuss QML techniques in supervised learning tasks like Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs). Risk management, credit scoring, fraud detection, and stock price prediction are among the financial applications that are taken into consideration. Additionally, we offer a summary of QML's drawbacks,

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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 (2023): 2402. http://dx.doi.org/10.3390/electronics12112402.

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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

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Zardini, Enrico, Enrico Blanzieri, and Davide Pastorello. "Implementation and empirical evaluation of a quantum machine learning pipeline for local classification." PLOS ONE 18, no. 11 (2023): e0287869. http://dx.doi.org/10.1371/journal.pone.0287869.

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In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k-nearest neighbors (k-NN) algorithm, of which several quantum variants have been proposed; nevertheless, they have not been employed yet as a preliminary step of other QML models. Instead, for the classical counterpart, a performance

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Shah, Pratham Kunjan. "Advancing the Quantum Internet: Integration of Quantum Machine Learning for Enhanced Efficiency and Reliability." Journal of Software Engineering and Simulation 10, no. 9 (2024): 24–31. http://dx.doi.org/10.35629/3795-10092431.

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Advancing the quantum internet through integrating Quantum Machine Learning (QML) presents a novel approach to addressing the significant challenges in quantum communication networks. Quantum internet, grounded in the principles of quantum mechanics, offers unparalleled security and computational power but faces hurdles such as error correction, efficient routing, and infrastructure maintenance. This paper first explores the foundational concepts that underpin the quantum internet, highlighting how quantum entanglement, superposition, and teleportation contribute to its potential. In the secon

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Karamchand, Gopalakrishna. "Quantum Machine Learning for Threat Detection in High-Security Networks." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 17, no. 02 (2025): 14–25. https://doi.org/10.18090/samriddhi.v17i02.05.

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The growing complexity and velocity of cyber threats in high-security environments such as defense, critical infrastructure, and intelligence networks necessitates a paradigm shift in threat detection capabilities. Traditional cybersecurity systems, including those enhanced by classical machine learning algorithms, often struggle to process and classify massive volumes of heterogeneous and encrypted data in real time. This shortcoming is particularly evident in the context of advanced persistent threats (APTs), polymorphic malware, and insider attacks, which require rapid adaptation and height

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Eze, Lauren, Umair B. Chaudhry, and Hamid Jahankhani. "Quantum-Enhanced Machine Learning for Cybersecurity: Evaluating Malicious URL Detection." Electronics 14, no. 9 (2025): 1827. https://doi.org/10.3390/electronics14091827.

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The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy o

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Subodh Nath Pushpak. "Quantum Machine Learning Technique for Insurance Claim Fraud Detection with Quantum Feature Selection." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 750–56. https://doi.org/10.52783/jisem.v10i8s.1193.

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This paper demonstrates a novel use of quantum machine learning (QML) algorithms for detecting fraudulent activities in the home insurance sector. Utilizing actual data and IBM Quantum processors through the Qiskit software stack, the study introduces an innovative method for selecting quantum features that are specifically designed to accommodate the limitations of Near Intermediate Scale Quantum (NISQ) technology by using the Quantum Support Vector Machine (QSVM) in conjunction with traditional machine learning techniques. A comprehensive comparison was conducted to evaluate their effectiven

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Neamah Abbas, Farah, Mohanad Ridha Ghanim, and Rafal Naser Saleh. "Subject Review:AI-Driven Security in Quantum Machine Learning:Vulnerabilities,Threats, and Defenses." International Journal of Engineering Research and Advanced Technology 11, no. 04 (2025): 01–22. https://doi.org/10.31695/ijerat.2025.4.1.

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Quantum Machine Learning (QML) has advanced significantly thanks to the combination of Quantum Computing (QC) with Artificial Intelligence (AI), hence releasing computational benefits over conventional methods. This synergy does, however, also bring fresh security flaws like adversarial attacks, quantum noise manipulation, and cryptographic weaknesses. This work offers a thorough investigation of QML security along with an examination of its special vulnerabilities resulting from hardware-induced faults, quantum variational circuits, and quantum data encoding. We methodically investigate adver

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Shashank Chaudhary. "Quantum Machine Learning: Bridging Quantum Computing and AI for Exponential Gains." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 126–35. https://doi.org/10.32628/cseit251112393.

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Quantum Machine Learning (QML) represents a convergent frontier where quantum computing meets artificial intelligence, offering transformative possibilities for computational challenges. This article explores the fundamental concepts, current applications, and future prospects of QML, examining how it addresses classical computational bottlenecks through quantum mechanical principles like superposition and entanglement. It analyzes core quantum computing architectures including Quantum Neural Networks, Variational Quantum Circuits, and Quantum Kernel Methods, highlighting their potential advan

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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 (2023): 035027. http://dx.doi.org/10.1088/2632-2153/acf096.

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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 generalisati

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Ranga, Deepak, Aryan Rana, Sunil Prajapat, Pankaj Kumar, Kranti Kumar, and Athanasios V. Vasilakos. "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions." Mathematics 12, no. 21 (2024): 3318. http://dx.doi.org/10.3390/math12213318.

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Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, includi

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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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Sharad S. Jagtap. "Reinventing Smart Farming Using Adaptive Quantum Machine Learning Model." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 453–61. https://doi.org/10.52783/jisem.v10i5s.665.

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This study investigates how precision agriculture and crop production predictions may be improved using Quantum Machine Learning (QML) models, namely the Variational Quantum Circuit (VQC). The VQC outperformed traditional linear regression and other quantum models such as Quantum Neural Networks (QNN) and Quantum Convolutional Neural Networks (QCNN) by using quantum computing's ability to analyze high-dimensional agricultural data. With the lowest Mean Squared Error (MSE: 28.00), Mean Absolute Error (MAE: 3.8), and greatest R-squared (R2: 0.97), the VQC successfully identified intricate relati

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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 (2022): 015034. http://dx.doi.org/10.1088/2632-2153/ac5997.

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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 fir

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Anisurrahman, Bashir Alam, and Muhammad Hamid. "Quantum Machine Learning for Drug Discovery: A Systematic Review." International Journal on Smart & Sustainable Intelligent Computing 2, no. 2 (2025): 80–87. https://doi.org/10.63503/j.ijssic.2025.158.

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Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum m

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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 (2021): 015020. http://dx.doi.org/10.1088/2058-9565/ac3c53.

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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

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Masoudian, Ali, Uffe Jakobsen, and Mohammad Hassan Khooban. "Emulation of Variational Quantum Circuits on Embedded Systems for Real-Time Quantum Machine Learning Applications." Designs 9, no. 4 (2025): 87. https://doi.org/10.3390/designs9040087.

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This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demons

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Raghavender, Maddali. "Quantum Machine Learning for Ultra-Fast Query Execution in High-Dimensional SQL Data Systems." International Journal of Leading Research Publication 3, no. 4 (2022): 1–13. https://doi.org/10.5281/zenodo.15107548.

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The new Quantum Machine Learning (QML) paradigm for highly efficient query execution in high-dimensional SQL data systems and Conventional database query execution is plagued by performance bottlenecks because of the explosive nature of structured data and intricate query optimization issues. The new QML-based methodology uses quantum algorithms to accelerate query processing by exploiting parallel computation, quantum-aided indexing, and probabilistic data access. With the incorporation of quantum-enhanced optimization methods, the framework achieves remarkable query execution time reduction,

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Thakare, Pratik Manoj. "Bridging the Gap Between Quantum Computing and Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27848.

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Quantum machine learning (QML) holds the potential to transform various industries, yet its widespread adoption faces formidable challenges. This paper provides a condensed exploration of these challenges and opportunities. I delve into: • Hardware Limitations: Present quantum computers are constrained in qubit count and gate quality, posing obstacles to real-world QML implementation. We dissect the implications of these limitations on QML computations. • Error Correction: Quantum systems are prone to errors stemming from hardware noise and gate imperfections. We scrutinize the strategies to m

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Hari Krishn Gupta, Naveen Kumar Vayyasi, and Jagadeesh Thiruveedula. "Quantum Machine Learning Approaches for Real-Time Market Pattern Recognition in High-Frequency Trading: A Banking Sector Application." International Research Journal on Advanced Science Hub 7, no. 06 (2025): 617–22. https://doi.org/10.47392//irjash.2025.072.

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Recognizing market trends in HFT gets simpler and faster thanks to quantum machine learning (QML) which also has the power to exceed classical model boundaries. Using quantum kernel methods and variational quantum circuits, QML can analyze high-dimensional financial information such as tick-by-tick data and order book samples, in the superposition state simultaneously. Comparative work proves that quantum-enhanced models have better accuracy, can detect patterns better and perform faster decisions on near-term quantum devices than classical ones. To be useful in banking-sector HFT, QML needs t

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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 (2022): 375–80. http://dx.doi.org/10.37391/ijeer.100252.

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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 superconductin

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Elicherla, Sivananda Lahari Reddy, and V. Ravindra Reddy. "The Role of Quantum Computing in Addressing the Big Data Problem in AI with Scaling Machine Learning Models: A Study." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem33068.

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The exponential growth of big data in artificial intelligence (AI) has posed significant challenges for scaling machine learning (ML) models efficiently. Classical computing methods struggle to process vast datasets and optimize complex ML models in a feasible timeframe. Quantum computing offers a promising paradigm shift by leveraging quantum mechanics to perform computations at unprecedented scales and speeds. This study explores the role of quantum computing in addressing the big data problem in AI, with a focus on its potential to enhance the scalability of ML models. Key topics include qu

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Tiwo, Olufisayo Juliana. "Quantum Machine Learning for Secure Financial Forecasting: Mitigating Data Breaches and Adversarial Exploits." Asian Journal of Research in Computer Science 18, no. 4 (2025): 154–75. https://doi.org/10.9734/ajrcos/2025/v18i4613.

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Quantum Machine Learning (QML) offers a transformative approach to financial forecasting by enhancing predictive accuracy and cybersecurity resilience. This study evaluates QML’s effectiveness using financial market data from Yahoo Finance, comparing Quantum Long Short-Term Memory (QLSTM) to classical LSTM and ARIMA models. Security vulnerabilities were assessed using the IEEE DataPort adversarial attack dataset, while encryption performance was analyzed using Quantum Key Distribution (QKD) data from NIST. Experimental results demonstrate that QLSTM outperforms classical models, achieving lowe

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Pushpak, Subodh Nath, Sarika Jain, and Siddharth Kalra. "Quantum machine learning optimization using Koopman operator technique." Periodicals of Engineering and Natural Sciences (PEN) 13, no. 2 (2025): 327–36. https://doi.org/10.21533/pen.v13.i2.317.

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Quantum machine learning (QML) is a nascent field showing great potential in addressing complex problems. QML algorithms aim to combine the qubit’s properties, like entanglement, interference, and superposition, to perform better than any classical computation in specific tasks. This paper explores a new way of employing the Koopman operator to demonstrate its application to quantum optimization in quantum machine learning. Koopman operator-based quantum optimization has applications in various dynamical systems, where it can optimally capture the full nonlinear behavior of a system [1]. This

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Hilmy, Nur Amalina Rahmaputri, and Muhamad Akrom. "IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT." Networking Engineering Research Operation 9, no. 2 (2024): 119–26. https://doi.org/10.21107/nero.v9i2.27781.

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Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, re

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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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Belis, Vasilis, Samuel González-Castillo, Christina Reissel, et al. "Higgs analysis with quantum classifiers." EPJ Web of Conferences 251 (2021): 03070. http://dx.doi.org/10.1051/epjconf/202125103070.

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We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limit

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