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cs.LG2025
Quantum-Boosted High-Fidelity Deep Learning
Feng-ao Wang, Shaobo Chen, Yao Xuan +12
A fundamental limitation of probabilistic deep learning is its predominant reliance on Gaussian priors. This simplistic assumption prevents models from accurately capturing the com…
cs.LG2025
Towards Provable and Scalable Training of Quantized Neural Networks with Ising Optimization
Wenxin Li, Chuan Wang, Hongdong Zhu +4
Training quantized neural networks remains fundamentally challenging due to non-convex loss landscapes and discrete parameter spaces. We introduce an exact Quadratic Constrained Bi…
quant-ph2025
QAMA: Scalable Quantum Annealing Multi-Head Attention Operator for Deep Learning
Peng Du, Jinjing Shi, Wenxuan Wang +3
Attention mechanisms underpin modern deep learning, while the quadratic time and space complexity limit scalability for long sequences. To address this, Quantum Annealing Multi-Hea…