Hybrid Quantum-Classical Machine Learning For Scalable AI Applications
DOI:
https://doi.org/10.29284/9yngyq55Keywords:
AI, QCNNs, QVCs, HQML, energy limitations.Abstract
Background: The emergent need for scalable artificial intelligence (AI) models emphasizes the shortcomings of traditional machine learning in dealing with big, complicated datasets owing to computation and energy limitations.
Objective: The architecture takes advantage of quantum encoding methods like amplitude and angle encoding for compact feature representation, with classical preprocessing providing compatibility and stability.
Methods: To overcome these issues, this research introduces a Hybrid Quantum-Classical Machine Learning (HQML) framework that combines Quantum Variational Classifiers (QVCs) and Quantum Convolutional Neural Networks (QCNNs) with classical deep learning layers.
Results: Experiments were performed on pre-curated datasets taken from the Scikit Machine Learning Repository, with hybrid models compared to classical Convolutional Neural Networks (CNNs). The implementation was done with PennyLane using TensorFlow Quantum for compatibility with noisy intermediate-scale quantum (NISQ) machines. The comparative analysis identified that the suggested HQML model achieves significant performance gains, as classification accuracy increased from 73 percent in the baseline classical CNN to 98% in the hybrid model. Precision also went up from 77 to 96 percent, recall from 81 to 100 percent, and F1-score from 79 to 98 percent. These findings confirm the ability of the model to leverage quantum benefits for extracting complicated data distributions without losing stability through classical components. The findings confirm the viability of HQML to real-world finance, healthcare, and intelligent automation applications where scalability and precision matter.
Conclusion: This research establishes that quantum-classical integration not only minimizes training overhead but also attains near-perfect classification efficiency with constrained qubit resources. The research aids in filling the gap between theoretical quantum potential and deployable practical AI, opening the door for next-generation quantum-boosted intelligence.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
