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

Exploring the Frontiers of Quantum Machine Learning: State of the Art Challenges and Applications

Mikel Prieto

Published Jul 6, 2026• 0 citations
Publisher
DOI
10.13140/rg.2.2.14774.05440

Related papers

Exploring the Frontier: Quantum Machine Learning Algorithms

Hiep L. Thi

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

Exploring the Frontiers of Quantum Machine Learning: A New Era in AI and Computation

Latika Kharb, Deepak Chahal

In the realm of artificial intelligence (AI), the integration of quantum computing with machine learning (ML) has sparked a significant shift in how we approach problems that were once deemed computationally infeasible. Quantum Machine Learning (QML) represents an exciting intersection of quantum mechanics and machine learning, potentially revolutionizing areas such as optimization, pattern recognition, and data analysis. Although quantum computing itself is still in its infancy, the synergy between quantum theory and classical machine learning algorithms promises to unlock new capabilities that classical computing could never achieve. This paper delves into the latest advancements in Quantum Machine Learning, its theoretical underpinnings, practical applications, and the challenges that researchers face as they explore this rapidly growing field.

International Scientific Journal of Engineering and Management•Mar 16, 2025•0 citations

A non-review of Quantum Machine Learning: trends and explorations

Vedran Dunjko, Péter Wittek

•Mar 17, 2020•80 citations

Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision

Essam Halim Houssein, Zainab Abohashima, Mohamed Elhoseny +1 more

Expert Systems with Applications•Jan 20, 2022•155 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

Quantum machine learning: Classifications, challenges, and solutions

Wei Lü, Yang Lu, Jin Li +3 more

Journal of Industrial Information Integration•Nov 1, 2024•31 citations
In the realm of artificial intelligence (AI), the integration of quantum computing with machine learning (ML) has sparked a significant shift in how we approach problems that were once deemed computationally infeasible. Quantum Machine Learning (QML) represents an exciting intersection of quantum mechanics and machine learning, potentially revolutionizing areas such as optimization, pattern recognition, and data analysis. Although quantum computing itself is still in its infancy, the synergy between quantum theory and classical machine learning algorithms promises to unlock new capabilities that classical computing could never achieve. This paper delves into the latest advancements in Quantum Machine Learning, its theoretical underpinnings, practical applications, and the challenges that researchers face as they explore this rapidly growing field.
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.