Quantum machine learning : Developing hybrid quantum-classical algorithms for enhanced computational power

Patil, Rina Suresh; Adsul, Alpana Prashant; Bhati, Sonam Singh; Deshpande, Vivek; Krishnanjaneyulu, Payala; Mounika, Edupuganti

doi:10.47974/JDMSC-2543

Open Access Research Article

Quantum machine learning : Developing hybrid quantum-classical algorithms for enhanced computational power

Rina Suresh Patilrinapatil@gmail.comDepartment of Computer Technology Sanjivani K.B.P. PolytechnicKopargaon, Maharashtra, 423603, IndiaView full profile → , Alpana Prashant Adsulalpana.adsul@gmail.comDepartment of Computer Engineering Dr. D.Y. Patil College of Engineering and InnovationPune, Maharashtra, 410507, IndiaView full profile → , Sonam Singh Bhatisonam.singh@niu.edu.inDepartment of Computer Science & Engineering Noida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile → , ^*Vivek DeshpandeCorresponding authorvivek.deshpande@vit.eduVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Payala Krishnanjaneyulukrishna54888@gmail.comDepartment of Computer Engineering Koneru Lakshmaiah Education Foundation BowrampetHyderabad, Telangana, 500090, IndiaView full profile → , Edupuganti Mounikaedupugantimounika@kluniversity.inDepartment of Computer Engineering Koneru Lakshmaiah Education FoundationVaddeswaram, Andhra Pradesh, 522302, IndiaView full profile →

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pp. 923–931Vol. 29Issue 2-BFebruary 2026DOI: 10.47974/JDMSC-2543 Crossmark XML

Received:: 14 May 2025
Published Online:: 31 Dec 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2543
Pages:: 923–931

Abstract

The increasing need for machine learning to run with less computer resources is causing quantum computing to be considered as a viable substitute for conventional computing. This paper presents a hybrid quantum-classical system that improves learning by means of a classical optimisation loop combined with parameterised quantum circuits. While conventional procedures handle parameter updates and convergence, the proposed approach stores and processes high-dimensional data using quantum variational circuits. Qiskit is used by us to construct this architecture and run it on conventional datasets like Iris, MNIST (binary), CIFAR-100 (subset), HIGGS, and GTEx (gene expression). Particularly for jobs with complex, nonlinear patterns, the findings indicate that the mixed approach is as accurate as or more accurate than classical baselines. Real quantum hardware also works well with the system; noise has no impact on its precision. This work demonstrates how combined quantum-classical learning models might circumvent hardware constraints and provide genuine quantum benefits for applications requiring large data processing.

Keywords

Quantum machine learning Hybrid algorithms Variational quantum circuit Quantum-classical optimization Qiskit NISQ devices

Subject Classifications

68M25

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