Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming
This work addresses computational efficiency for researchers and practitioners in computer vision and Big Data analytics, but it is incremental as it builds on existing hybrid quantum-classical techniques.
The paper tackled the problem of high computational demands in training Convolutional Neural Networks (CNNs) by proposing a hybrid optimization method combining Unconstrained Binary Quadratic Programming (UBQP) with Stochastic Gradient Descent (SGD), resulting in a 10-15% accuracy improvement over a standard baseline on the MNIST dataset while maintaining similar execution times.
Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets. Conventional training via back-propagation (BP) with losses like Mean Squared Error or Cross-Entropy often requires extensive iterations and may converge sub-optimally. Quantum computing offers a promising alternative by leveraging superposition, tunneling, and entanglement to search complex optimization landscapes more efficiently. In this work, we propose a hybrid optimization method that combines an Unconstrained Binary Quadratic Programming (UBQP) formulation with Stochastic Gradient Descent (SGD) to accelerate CNN training. Evaluated on the MNIST dataset, our approach achieves a 10--15\% accuracy improvement over a standard BP-CNN baseline while maintaining similar execution times. These results illustrate the potential of hybrid quantum-classical techniques in High-Performance Computing (HPC) environments for Big Data and Deep Learning. Fully realizing these benefits, however, requires a careful alignment of algorithmic structures with underlying quantum mechanisms.