用量子优化方法加速卷积神经网络训练，提升精度且不增加时间开销。
Performance Analysis of Convolutional Neural Network By Applying Unconstrained Binary Quadratic Programming
- 将CNN训练转化为无约束二元二次规划问题，结合随机梯度下降
- 在MNIST上比传统反向传播提升10%~15%准确率，耗时相当
- 适合关注量子-经典混合算法在大模型训练中应用的读者
卷积神经网络（CNN）在计算机视觉和大数据分析中至关重要，但在大规模数据集上训练需消耗大量计算资源。传统的基于反向传播（BP）和均方误差或交叉熵损失的训练方式通常需要大量迭代，且可能陷入次优收敛。量子计算通过叠加、隧穿和纠缠等特性，能更高效地搜索复杂优化空间，提供新路径。本文提出一种混合优化方法，将无约束二元二次规划（UBQP）与随机梯度下降（SGD）结合，以加速CNN训练。在MNIST数据集上的实验表明，该方法相比标准BP-CNN基线实现10%~15%的准确率提升，同时保持相近的执行时间。结果展示了混合量子-经典技术在高性能计算（HPC）环境中处理大数据与深度学习的潜力。然而，要充分实现这些优势，仍需算法结构与底层量子机制的精细对齐。
原文摘要 · Abstract (English)
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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