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

Lecture notes in computer science

Quantum Machine Learning: Hybrid System of Quantum and Classical Computing

Lian Peng, Meikang Qiu A, Chong Li, Zhihui Lu

Published Jan 1, 2025• 2 citations
Publisher
DOI
10.1007/978-981-96-8725-1_35

Related papers

Explainability of Decision-Making in Hybrid Quantum-Classical Classification Models

Sayali Ramesh Kote

This research paper explores the explainability of decision-making in hybrid quantum–classical classification models. Quantum Machine Learning (QML) combines the power of quantum computing with classical machine learning techniques to solve complex computational problems. However, understanding how these hybrid models make decisions remains a significant challenge. The study focuses on analyzing hybrid quantum–classical architectures and evaluating methods that improve the interpretability of their predictions. Techniques from Explainable Artificial Intelligence (XAI) are applied to investigate the internal decision-making processes of the models. The research also compares classical machine learning approaches with quantum-enhanced models to understand their performance and transparency. The results demonstrate that explainability techniques can help researchers better interpret model predictions, identify important features, and improve trust in hybrid quantum–classical systems. This work contributes to the development of transparent and reliable quantum machine learning applications in areas such as healthcare, finance, and data analytics.

•Mar 9, 2026•0 citations

Hybrid Quantum and Classical Machine Learning

Zeph Hardie

Queensland University of Technology eBooks•Jan 1, 2026•0 citations

Hybrid Quantum-Classical Machine Learning Models: Powering the Future of AI

Mohan Raja Pulicharla

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 portfolios. However, current quantum hardware limitations necessitate a hybrid approach: Hybrid Quantum-Classical Machine Learning Models (HQCLML). The convergence of quantum computing and classical machine learning has sparked significant interest in the development of hybrid quantum-classical machine learning models. This research explores the synergy between quantum and classical paradigms, aiming to leverage the strengths of both to enhance the capabilities of machine learning algorithms. The paper provides an in-depth overview of quantum computing principles, classical machine learning models, and the foundational concepts that form the basis for hybrid models. Various approaches to integrating quantum computing into machine learning are discussed, emphasizing the potential advantages in solving complex problems, particularly those involving large-scale optimization or exponential search spaces. The study delves into quantum machine learning algorithms, showcasing examples such as Quantum Support Vector Machines and Quantum Neural Networks. Case studies and applications of hybrid models are presented to illustrate instances where quantum enhancements outperform classical counterparts. While highlighting the promising achievements, the paper also addresses the current challenges and limitations associated with hybrid models, including practical considerations, error rates, and the impact of decoherence in quantum computing. As quantum hardware technologies continue to advance, the paper explores the current landscape of quantum processors and their implications for hybrid models. The discussion extends to future directions, offering predictions for the development of hybrid quantum-classical machine learning models. Emerging technologies and potential breakthroughs are considered, presenting a forward-looking perspective on the evolving landscape of artificial intelligence research. In conclusion, the research underscores the significance of hybrid quantum-classical machine learning models as a transformative avenue for addressing complex computational problems. The synergy between quantum and classical approaches holds immense potential for advancing the field of machine learning, opening new horizons for solving problems that were once deemed computationally intractable.

Journal of Science & Technology•Jan 13, 2023•35 citations

Quantum Machine Learning: Bridging Classical and Quantum Frontiers

Sardar M. N. Islam, Prem Kinger, Neda Fatima +1 more

Innovations in Sustainable Technologies and Computing•Jan 1, 2024•4 citations

Hybrid Quantum-Classical Machine Learning for Lithography Hotspot Detection

Yuan-Fu Yang, Min Sun

In advanced semiconductor process technology, lithography hotspot detection has become an essential task in design for manufacturability. The ability to detect and repair lithography hotspots that can affect printability is critical to improving yield and productivity. Machine learning technology has become a powerful tool in a variety of applications, from finance to manufacturing and computer vision. The use of quantum systems to process classical data using machine learning algorithms has created an emerging field of research, namely quantum machine learning (QML). We explore the possibility of converting a novel machine learning model to a hybrid quantum-classical machine learning that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. In addition, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities. Then we compare their training performance to quantify the expected benefits. These results can be used to build a future roadmap to develop circuit-based hybrid quantum-classical machine learning for lithography hotspot detection.

ASMC proceedings•May 2, 2022•5 citations

Quantum Machine Learning and its Applications

WORLD SCIENTIFIC eBooks•Jul 9, 2024•0 citations
This research paper explores the explainability of decision-making in hybrid quantum–classical classification models. Quantum Machine Learning (QML) combines the power of quantum computing with classical machine learning techniques to solve complex computational problems. However, understanding how these hybrid models make decisions remains a significant challenge. The study focuses on analyzing hybrid quantum–classical architectures and evaluating methods that improve the interpretability of their predictions. Techniques from Explainable Artificial Intelligence (XAI) are applied to investigate the internal decision-making processes of the models. The research also compares classical machine learning approaches with quantum-enhanced models to understand their performance and transparency. The results demonstrate that explainability techniques can help researchers better interpret model predictions, identify important features, and improve trust in hybrid quantum–classical systems. This work contributes to the development of transparent and reliable quantum machine learning applications in areas such as healthcare, finance, and data analytics.
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 portfolios. However, current quantum hardware limitations necessitate a hybrid approach: Hybrid Quantum-Classical Machine Learning Models (HQCLML). The convergence of quantum computing and classical machine learning has sparked significant interest in the development of hybrid quantum-classical machine learning models. This research explores the synergy between quantum and classical paradigms, aiming to leverage the strengths of both to enhance the capabilities of machine learning algorithms. The paper provides an in-depth overview of quantum computing principles, classical machine learning models, and the foundational concepts that form the basis for hybrid models. Various approaches to integrating quantum computing into machine learning are discussed, emphasizing the potential advantages in solving complex problems, particularly those involving large-scale optimization or exponential search spaces. The study delves into quantum machine learning algorithms, showcasing examples such as Quantum Support Vector Machines and Quantum Neural Networks. Case studies and applications of hybrid models are presented to illustrate instances where quantum enhancements outperform classical counterparts. While highlighting the promising achievements, the paper also addresses the current challenges and limitations associated with hybrid models, including practical considerations, error rates, and the impact of decoherence in quantum computing. As quantum hardware technologies continue to advance, the paper explores the current landscape of quantum processors and their implications for hybrid models. The discussion extends to future directions, offering predictions for the development of hybrid quantum-classical machine learning models. Emerging technologies and potential breakthroughs are considered, presenting a forward-looking perspective on the evolving landscape of artificial intelligence research. In conclusion, the research underscores the significance of hybrid quantum-classical machine learning models as a transformative avenue for addressing complex computational problems. The synergy between quantum and classical approaches holds immense potential for advancing the field of machine learning, opening new horizons for solving problems that were once deemed computationally intractable.
In advanced semiconductor process technology, lithography hotspot detection has become an essential task in design for manufacturability. The ability to detect and repair lithography hotspots that can affect printability is critical to improving yield and productivity. Machine learning technology has become a powerful tool in a variety of applications, from finance to manufacturing and computer vision. The use of quantum systems to process classical data using machine learning algorithms has created an emerging field of research, namely quantum machine learning (QML). We explore the possibility of converting a novel machine learning model to a hybrid quantum-classical machine learning that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. In addition, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities. Then we compare their training performance to quantify the expected benefits. These results can be used to build a future roadmap to develop circuit-based hybrid quantum-classical machine learning for lithography hotspot detection.