Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming

• URL: http://arxiv.org/abs/2506.00247v1
• Date: Fri, 30 May 2025 21:25:31 GMT
• Title: Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming
• Authors: Aasish Kumar Sharma, Sanjeeb Prashad Pandey, Julian M. Kunkel,
• Abstract summary: Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets.<n>We propose a hybrid optimization method that combines Unconstrained Binary Quadratic Programming (UBQP) with Gradient Descent (SGD) to accelerate CNN training.<n>Our approach achieves a 10--15% accuracy improvement over a standard BP-CNN baseline while maintaining similar execution times.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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