A thorough comparison of classical and quantum machine learning models using the MNIST dataset reveals key differences in performance. Sudip Vhaduri and colleagues at University of Alabama evaluated accuracy, runtime, parameter count, and memory requirements across varying feature dimensions and sample sizes. The findings demonstrate that quantum support vector machines consistently achieve higher accuracy than classical support vector machines. Furthermore, quantum convolutional neural networks exhibit sharply improved parameter and memory efficiency, requiring up to 94% fewer parameters and 75% less memory, although with increased runtime. This multidimensional benchmarking study highlights the potential for quantum models to outperform classical models, particularly with higher dimensionality or larger datasets, and provides valuable insights into practical operating parameters for quantum machine learning. Quantum neural networks exhibit substantial gains in parameter efficiency and classification Quantum Convolutional Neural Networks (QCNNs) now require approximately 94% fewer parameters and 75% less memory than Classical Convolutional Neural Networks (CCNNs) at higher feature counts, a reduction previously unattainable with classical deep learning approaches. This efficiency unlocks the potential for deploying complex image recognition models on resource-constrained devices, overcoming a significant barrier in fields like automated transport and cybersecurity. Achieving comparable classification accuracy exceeding 0.96 with 64 features and 60,000 samples, the QCNN’s reduced memory footprint represents a substantial advancement in model scalability. The MNIST dataset, comprising 70,000 labelled grayscale images of handwritten digits, served as the benchmark for this comparison. Classical convolutional neural networks typically rely on numerous weighted connections between layers, leading to a high parameter count and substantial memory requirements, particularly when dealing with high-resolution images or complex feature extraction. QCNNs, leveraging principles of quantum superposition and entanglement, represent data in a fundamentally different way, allowing for a more compact and efficient representation. This is achieved through the use of quantum circuits that perform operations on qubits, the