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

Variational Quantum-Classical Optimization for Molecular Feature Generation: A Hybrid Machine Learning Approach

Abiodun Emmanuel Sojobi

Published Dec 17, 2025• 0 citations
Publisher
DOI
10.5281/zenodo.19897385

Related papers

Hybrid Quantum-Classical Machine Learning Optimization using GPU-Accelerated Variational Circuits

Anoushka Vats

•Jan 1, 2025•0 citations

Challenges and opportunities for hybrid quantum-classical machine learning and optimization

Masoud Mohseni

Bulletin of the American Physical Society•Mar 2, 2020•0 citations

HYBRID QUANTUM-CLASSICAL ARCHITECTURES FOR OPTIMIZING MACHINE LEARNING TRAINING SPEED AND ACCURACY IN BRAIN TUMOR DETECTION

Prakash Mishra, Rakesh Kumar

Brain tumor classification remains a challenging task due to the complex heterogeneity of glioma characteristics and the limitations of conventional machine learning approaches in effectively integrating multimodal biomedical data. Recent advances in quantum machine learning have demonstrated promising capabilities for handling high-dimensional data; however, existing models often suffer from limited feature representation, insufficient multimodal integration, and optimization challenges. This paper proposes an Enhanced Hybrid Quantum–Classical Architecture (EHQCA) for glioma classification by combining MRI imaging features and TCGA molecular biomarkers within a unified learning framework. The proposed methodology employs ResNet-50 for imaging feature extraction, ensemble feature selection for molecular biomarkers, attention-based multimodal fusion, adaptive amplitude encoding, and a Multi-Layer Variational Quantum Classifier (ML-VQC). A hybrid Adam-SPSA optimization strategy is introduced to improve convergence speed and training stability. Experimental analysis demonstrates that the proposed EHQCA framework achieves superior classification performance, attaining 91.2% accuracy while reducing training epochs compared with existing quantum and classical models. The results highlight the effectiveness of multimodal fusion and quantum-enhanced learning for improving brain tumor diagnosis and supporting precision medicine applications

International Journal of Computer Information Systems and Industrial Management Applications•Jun 20, 2026•0 citations

Quantum-Enhanced Cloud Intelligence: Hybrid Variational Models for Next-Generation Scalable Machine Learning

Quentil Varano, Iselphine Draxmont, Zalmera O. Nyvrenn

This research investigates a quantum enhanced cloud intelligence framework based on hybrid variational models that integrate Variational Quantum Algorithms (VQAs), Parameterized Quantum Circuits (PQCs), and classical machine learning optimization. The study aims to address the scalability and computational limitations of conventional cloud-based machine learning by leveraging the expressive power of quantum feature spaces and entanglement-driven representations. A structured methodology is presented, encompassing hybrid model design, dataset preparation, quantum circuit construction, and the implementation of a cloud-integrated training loop. Performance benchmarking across high-dimensional datasets demonstrates that the proposed hybrid approach can achieve faster training, improved model accuracy, and enhanced energy efficiency compared to classical baselines. The research further outlines the practical challenges posed by NISQ-era hardware, noise sensitivity, cloud latency, and hybrid optimization instability. Despite these limitations, the findings reveal strong potential for deploying quantum-assisted intelligence in real-time analytics, complex optimization problems, healthcare diagnostics, autonomous systems, and cybersecurity applications. This study contributes a unified integration framework, novel empirical benchmarks, and a practical roadmap for advancing quantum cloud synergy, positioning hybrid variational systems as a promising foundation for next-generation scalable machine learning.

Jurnal Teknik Informatika C I T Medicom•Jul 30, 2025•0 citations

Quantum Optimization Approach for Feature Selection in Machine Learning

Gérard Fleury, Bogdan Vulpescu, Philippe Lacomme

Lecture notes in computer science•Jan 1, 2024•3 citations

Quantum–Classical Neural Networks with Variational Circuits for Molecular Docking in Drug Discovery

K. Kalaiselvi, G. Husna Khouser, Kavitha A K +3 more

International Conference on Cognitive Computing and Networking Systems (ICC-CNS)•Jun 1, 2026•0 citations
Brain tumor classification remains a challenging task due to the complex heterogeneity of glioma characteristics and the limitations of conventional machine learning approaches in effectively integrating multimodal biomedical data. Recent advances in quantum machine learning have demonstrated promising capabilities for handling high-dimensional data; however, existing models often suffer from limited feature representation, insufficient multimodal integration, and optimization challenges. This paper proposes an Enhanced Hybrid Quantum–Classical Architecture (EHQCA) for glioma classification by combining MRI imaging features and TCGA molecular biomarkers within a unified learning framework. The proposed methodology employs ResNet-50 for imaging feature extraction, ensemble feature selection for molecular biomarkers, attention-based multimodal fusion, adaptive amplitude encoding, and a Multi-Layer Variational Quantum Classifier (ML-VQC). A hybrid Adam-SPSA optimization strategy is introduced to improve convergence speed and training stability. Experimental analysis demonstrates that the proposed EHQCA framework achieves superior classification performance, attaining 91.2% accuracy while reducing training epochs compared with existing quantum and classical models. The results highlight the effectiveness of multimodal fusion and quantum-enhanced learning for improving brain tumor diagnosis and supporting precision medicine applications
This research investigates a quantum enhanced cloud intelligence framework based on hybrid variational models that integrate Variational Quantum Algorithms (VQAs), Parameterized Quantum Circuits (PQCs), and classical machine learning optimization. The study aims to address the scalability and computational limitations of conventional cloud-based machine learning by leveraging the expressive power of quantum feature spaces and entanglement-driven representations. A structured methodology is presented, encompassing hybrid model design, dataset preparation, quantum circuit construction, and the implementation of a cloud-integrated training loop. Performance benchmarking across high-dimensional datasets demonstrates that the proposed hybrid approach can achieve faster training, improved model accuracy, and enhanced energy efficiency compared to classical baselines. The research further outlines the practical challenges posed by NISQ-era hardware, noise sensitivity, cloud latency, and hybrid optimization instability. Despite these limitations, the findings reveal strong potential for deploying quantum-assisted intelligence in real-time analytics, complex optimization problems, healthcare diagnostics, autonomous systems, and cybersecurity applications. This study contributes a unified integration framework, novel empirical benchmarks, and a practical roadmap for advancing quantum cloud synergy, positioning hybrid variational systems as a promising foundation for next-generation scalable machine learning.