深度量子演变神经网络具有增强的可训练性和梯度传播
Muhammad Kashif1,2, Muhammad Shafique3,4
1eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, United Arab Emirates. muhammadkashif@nyu.edu.
Scientific reports
|July 2, 2025
概括
本研究介绍了量子卷积神经网络 (QuNN) 的可训练的量子卷积层和残余块,以增强深度学习能力. 这些进步提高了复杂量子神经网络的梯度流和训练效率.
科学领域:
- 量子计算是一种量子计算.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的量子演变神经网络 (QuNN) 使用静态层,限制特征提取.
- 深度QuNN与梯度流作斗争,阻碍了有效的训练和优化.
研究的目的:
- 通过引入可训练的量子演变层来提高QuNN的性能.
- 通过改善梯度流程,解决深度QuNN中的培训挑战.
主要方法:
- 开发可训练的量子演变层,以增强特征提取.
- 拟议的剩余量子演变神经网络 (ResQuNNs) 具有跳过连接.
- 研究了残留块的最佳放置,以实现高效的训练.
主要成果:
- 可训练层显著提高了QuNN的可扩展性和学习潜力.
- ResQuNNs有效地增强了深度网络中的梯度流.
- 经验证据证实,战略剩余块的放置优化了训练.
结论:
- 可训练层和残留学习使QuNN中的有效深度学习成为可能.
- 这项工作促进了量子深度学习的理论和实际应用.
- 这些发现为更强大的量子神经网络架构铺平了道路.
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