Experimental Comparison of Quantum Support Vector Machines and Neural Networks for Classification Tasks on Small Datasets

In recent years, significant breakthroughs have been made in both artificial intelligence and quantum information science. The synergy between these domains has opened new frontiers for innovation, offering transformative potential across research and industry. A growing body of work has explored quantum-enhanced learning models, with many proposals validated through simulations on classical hardware emulating quantum systems. Yet, the absence of uniform evaluation standards makes it difficult to objectively assess the performance of these emerging techniques. This paper explores the applicability of quantum-augmented learning methods to tangible, albeit small-scale, problem-solving scenarios. Focusing on practical implementation, we empirically evaluate two hybrid approaches: support vector machines with quantum-inspired kernel methods and neural networks adapted for quantum processing. To ensure robust analysis, our experiments leverage five diverse datasets, each processed using multiple quantum state encoding schemes. Through systematic comparison, we aim to clarify the strengths and limitations of current quantum-assisted models in handling real-world data challenges.