A Computationally-Efficient Hybrid Quantum-Classical Algorithm for Robust Classification in High-Complexity Data Environments on Commodity Hardware

Abstract The practical application of quantum machine learning algorithms, such as Quantum Neural Networks (QNNs), is currently constrained by their dependence on nascent, error-prone, and resource-intensive quantum hardware. This research introduces and validates a novel hybrid quantum- classical (HQC) algorithm engineered to deliver robust classification performance on modest, commodity classical hardware, thereby circumventing the quantum hardware bottleneck. Our multi-stage architecture strategically integrates advanced classical statistical analysis (Mahalanobis distance), a quantum-inspired kernel method (a hardened Quantum Support Vector Machine), and a quantum-inspired anomaly detection model (a Quantum Autoencoder). This ensemble was rigorously benchmarked on a synthetic dataset specifically engineered for extreme classification dinjculty (85-90%), characterized by high dimensionality, low informative features, and complex, multi- cluster class structures. The HQC algorithm achieved a notable weighted average accuracy of 42.66% across multiple experimental runs. This result is particularly significant when contextualized: it was achieved on a legacy classical server (Dell PowerEdge T110 II), yet it competitively approaches the performance envelope of state-of-the-art QNNs (55-70%) that require genuine quantum processors to exploit phenomena like superposition and entanglement. This work presents a compelling case for the strategic value of hybrid models as a pragmatic, resource- enjcient, and immediately deployable solution. It effectively bridges the gap between the theoretical promise of quantum computing and the practical demands of current computational ecosystems, offering a viable and powerful pathway toward the democratization of complex problem- solving