Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Advances in computers

Quantum machine learning: Exploring the nexus of quantum computing and intelligent algorithms

J. Singh, Meenakshi Sharma, Ramendra Pratap Singh, Gundala Venkata Rama Lakshmi, Minal Bafna, Neeraj Sirohi

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.1016/bs.adcom.2025.03.007

Related papers

Exploring the Frontier: Quantum Machine Learning Algorithms

Hiep L. Thi

Lecture notes in networks and systems•Jan 1, 2025•0 citations

Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI

Gaurav Kashyap

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 machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades. With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.

Journal of Artificial Intelligence & Cloud Computing•Feb 3, 2025•0 citations

Merging Artificial Intelligence (AI) and Machine Learning (ML) With Quantum Technology

Tarun Kumar Vashishth, Vikas Sharma, Kewal Krishan Sharma +1 more

Quantum machine learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics with machine learning algorithms, offering promising solutions to complex computational problems. This paper provides an extensive introduction to quantum machine learning, exploring the synergies between artificial intelligence and quantum technology. It begins by elucidating the foundational concepts of quantum computing, including quantum bits (qubits), quantum gates, superposition, and entanglement. Subsequently, the paper delves into the basic principles of machine learning and the challenges faced by classical methods in handling large-scale data and solving computationally intensive tasks. With a strong foundation in place, the focus shifts towards the integration of quantum mechanics with machine learning, showcasing the advantages and potential breakthroughs QML offers over classical approaches. The exploration of quantum algorithms for machine learning, such as quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum variational algorithms, reveals the power of quantum-enhanced techniques in data classification, regression, and clustering.

CRC Press eBooks•Aug 2, 2024•5 citations

Quantum Machine Learning: A Review and Current Status

Nimish Mishra, Manik Kapil, H R Rakesh +20 more

Advances in intelligent systems and computing•Sep 18, 2020•80 citations

Quantum Machine Learning: A Comparative Review of Algorithms and Applications

Amani Lamine, Ameni Mejri, Bilel Charfi

Lecture notes in networks and systems•Jan 1, 2026•0 citations

Quantum Machine Learning: The Confluence of Quantum Computing and AI

Kaledio Potter, JULIA ROSINSKI, LETHO KARL +1 more

Quantum Machine Learning (QML) represents the juncture of quantum computing and artificial intelligence, ushering in a new era of computation and data analysis. This abstract explores the convergence of these two groundbreaking fields and its implications for the future of AI.In QML, quantum bits (qubits) harness the unique properties of superposition and entanglement, enabling the simultaneous exploration of vast solution spaces. Quantum algorithms, such as Quantum Support Vector Machines and Quantum Neural Networks, promise exponential speedup for a range of AI tasks.However, QML is not without its challenges. Quantum hardware, including quantum processors and annealers, must contend with issues of error correction and scalability. Ethical and security considerations also loom large in the development and application of quantum machine learning.As quantum computing technology matures, QML holds the potential to redefine the boundaries of artificial intelligence, opening new frontiers in data analysis, optimization, and problem-solving. This abstract encapsulates the transformative possibilities and challenges presented by the confluence of quantum computing and AI in the realm of Quantum Machine Learning.

•Oct 20, 2023•3 citations
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 machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades. With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.
Quantum machine learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics with machine learning algorithms, offering promising solutions to complex computational problems. This paper provides an extensive introduction to quantum machine learning, exploring the synergies between artificial intelligence and quantum technology. It begins by elucidating the foundational concepts of quantum computing, including quantum bits (qubits), quantum gates, superposition, and entanglement. Subsequently, the paper delves into the basic principles of machine learning and the challenges faced by classical methods in handling large-scale data and solving computationally intensive tasks. With a strong foundation in place, the focus shifts towards the integration of quantum mechanics with machine learning, showcasing the advantages and potential breakthroughs QML offers over classical approaches. The exploration of quantum algorithms for machine learning, such as quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum variational algorithms, reveals the power of quantum-enhanced techniques in data classification, regression, and clustering.
Quantum Machine Learning (QML) represents the juncture of quantum computing and artificial intelligence, ushering in a new era of computation and data analysis. This abstract explores the convergence of these two groundbreaking fields and its implications for the future of AI.In QML, quantum bits (qubits) harness the unique properties of superposition and entanglement, enabling the simultaneous exploration of vast solution spaces. Quantum algorithms, such as Quantum Support Vector Machines and Quantum Neural Networks, promise exponential speedup for a range of AI tasks.However, QML is not without its challenges. Quantum hardware, including quantum processors and annealers, must contend with issues of error correction and scalability. Ethical and security considerations also loom large in the development and application of quantum machine learning.As quantum computing technology matures, QML holds the potential to redefine the boundaries of artificial intelligence, opening new frontiers in data analysis, optimization, and problem-solving. This abstract encapsulates the transformative possibilities and challenges presented by the confluence of quantum computing and AI in the realm of Quantum Machine Learning.