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February 2, 2026QuantaOpen Access

Quantum Machine Learning: A Review of Hybrid Classical-Quantum Approaches

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Authors

BPBhavesh B. PrajapatiDharmsinh Desai UniversityRPRiddhi B. PrajapatiGovernment of Gujarat

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Overview

This review demonstrates promising outcomes of hybrid classical-quantum learning, suggesting paths to quantum advantage in various fields.

Key Points

  • The aim is to explore hybrid approaches in quantum machine learning and their implications for future developments.
  • Review of existing literature on hybrid classical-quantum methods
  • Analysis of variational quantum circuits and hybrid neural networks
  • Examination of quantum kernel techniques and their applications
  • Hybrid approaches show promise in molecular modelling and materials discovery
  • Issues like noise and scalability hinder broader application
  • Recommendations for noise-aware algorithms and error-mitigation strategies are outlined

Cite This Study

Prajapati et al. (2026) studied this question.

synapsesocial.com/papers/6980ff49c1c9540dea81235bhttps://doi.org/10.12743/quanta.94
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