Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study

A comprehensive benchmarking study compares classical and quantum machine learning models for image recognition, finding that quantum models (QSVM and QCNN) achieve superior accuracy and efficiency in specific scenarios. While quantum neural networks require 94% fewer parameters than classical counterparts, they incur higher computational costs, suggesting practical quantum advantage exists only within defined operating windows.

This research addresses a critical question in emerging quantum computing: whether quantum machine learning delivers tangible advantages over matured classical approaches. The study's methodical comparison across multiple performance dimensions—accuracy, runtime, parameters, and memory—fills an important gap in quantum ML literature, which often lacks rigorous side-by-side benchmarking with controlled variables. The findings reveal a nuanced reality rather than a binary quantum victory. Quantum support vector machines demonstrate consistent accuracy improvements, particularly as data dimensionality increases, reaching 90% versus 85% accuracy at 1,000 samples. More striking, quantum convolutional neural networks achieve dramatic parameter and memory efficiency gains, requiring 94% less memory than classical CNNs while maintaining comparable accuracy above 96%. These efficiency improvements matter for edge computing and resource-constrained environments where parameter count directly impacts deployment feasibility. However, quantum models impose substantially higher computational overhead during execution, limiting their practical applicability to specific use cases rather than universal machine learning applications. The identified sweet spot—10 qubits and 200-500 samples for SVMs—indicates quantum advantage emerges within constrained operational parameters, not across all scenarios. This suggests quantum machine learning functions best as a specialized tool for particular problems rather than a wholesale replacement for classical methods. The research trajectory points toward hybrid classical-quantum approaches optimizing each paradigm's strengths.

• →Quantum machine learning models outperform classical counterparts in accuracy, with advantages expanding as feature dimensionality increases.
• →Quantum neural networks achieve 94% parameter reduction and 75% memory savings compared to classical CNNs at higher feature counts.
• →Practical quantum advantage emerges only within specific operating windows: approximately 10 qubits and 200-500 samples for SVM applications.
• →Despite superior efficiency, quantum models incur significantly higher computational runtime, presenting a trade-off between resource efficiency and execution speed.
• →Results suggest quantum ML functions best as a specialized solution for targeted problems rather than a universal replacement for classical machine learning.