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

arXiv: https://arxiv.org/abs/2506.00247 · HTML: https://mlsanity.com/paper/2506.00247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 5 of 5)

- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Simulated Quantum Annealing Can Be Exponentially Faster than Classical Simulated Annealing](https://mlsanity.com/paper/1601.03030/) (2016)
- [A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers](https://mlsanity.com/paper/1703.05364/) (2017)
- [Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers](https://mlsanity.com/paper/2301.00520/) (2023)

## Abstract (arXiv)

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.