Quantum Machine Learning as the Next Frontier of Computational Intelligence
DOI:
https://doi.org/10.70076/system.v1i1.107Keywords:
Quantum Machine Learning, Quantum Computing, Hybrid Quantum-Classical, Variational Quantum Circuits, Big Data Analytics, Quantum Neural NetworksAbstract
Quantum Machine Learning (QML) integrates quantum computing with machine learning to address high-dimensional and complex data that often exceed classical computational limits. By leveraging superposition and entanglement, QML enables enhanced computational parallelism that supports more efficient pattern recognition and optimization tasks. This study evaluates Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and hybrid variational algorithms using cloud-based IBM Quantum hardware. The results demonstrate an 8–12% improvement in accuracy and approximately 30% faster execution time compared with classical models. Furthermore, the hybrid approach exhibits strong resilience to hardware noise and decoherence. These findings confirm the feasibility of QML as a practical solution for big data analytics in healthcare, finance, and materials science. Continuous advancements in hardware scalability and noise mitigation are expected to strengthen QML’s role in industrial innovation and intelligent computing.
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