3 papers
quant-ph2025
Towards Fault-Tolerant Quantum Deep Learning: Designing and Analyzing Quantum ResNet and Transformer with Quantum Arithmetic and Linear Algebra Primitives
Xiao-Fan Xu, Cheng Xue, Xi-Ning Zhuang +8
Achieving a practical quantum speedup for deep neural networks (DNNs) remains a central yet elusive goal, hindered by the dual challenges of constructing deep architectures and the…
quant-ph2025
PolyQROM: Orthogonal-Polynomial-Based Quantum Reduced-Order Model for Flow Field Analysis
Yu Fang, Cheng Xue, Tai-Ping Sun +10
Quantum computing promises exponential acceleration for fluid flow simulations, yet the measurement overhead required to extract flow features from quantum-encoded flow field data…
cs.LG2024
A Step-by-step Introduction to the Implementation of Automatic Differentiation
Yu-Hsueh Fang, He-Zhe Lin, Jie-Jyun Liu +1
Automatic differentiation is a key component in deep learning. This topic is well studied and excellent surveys such as Baydin et al. (2018) have been available to clearly describe…