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Knowledge-Based Systems

Enhanced deep learning and quantum variational classifier for large-scale data analysis

Dyagala Naga Sudha, A. Anju, K. Ezhilarasi

Published Oct 11, 2025• 9 citations
Publisher
DOI
10.1016/j.knosys.2025.114611

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Quantum machine learning offers the ability to change the significant task of classifying high-dimensions data by taking advantage of infinite-dimensional, enriched state spaces granting a net benefit of quantum computation. We offer a variational quantum algorithm that accommodates many use cases in quantum computing to classify large-scale and high-dimensions data in real time and in cloud-all computing environments. We created our unique quantum circuits; we optimized the parameterized gates using either gradient methods or gradients-free methods and we investigated quantum kernels that provide the capability to identify embeddings in Hilbert space. Our experimental results using both customized synthetic data and benchmarks have shown that the variational quantum classifier was able to achieve comparability in accuracy to classical deep learning accuracy, while improving scale and robustness to noise. In addition, after we deployed the application in a cloud environment, we assessed latency, throughput and metrics from each cloud vendor, and we discovered that if the latency of the quantum accelerators can be controlled by a pipeline of optimized data and workloads, then they are eligible to satisfy the real-time requirement. Based on a theoretical and practical rapidly emerging basis, these results have indicated that variation quantum classifiers are a pathway for future dataintensive applications deployed in the quantum-cloud ecosystem.