10 papers
Rethinking Prediction Horizons: Decoupling Model Queries from State Updates in Few-Step MeanFlow
Tinglan Wang, Haixu Liu, Shaobo Chen +4
MeanFlow predicts interval-average velocities, but conventional few-step sampling ties the interval queried from the model to the duration of the state update. We separate these tw…
Q-DEQ: Discrete Solving and Quantization for Deep Equilibrium Models in Time Series Forecasting under Edge Deployment Coding Constraints
Ruotong Yang, Hongdong Zhu, Qi Gao +3
Edge deployment motivates forecasting models with compact parameter storage and low-bit representations. Deep equilibrium models (DEQs) obtain implicit depth by repeatedly applying…
A generalized variational quantum linear solver on photonic platform
Kang Gao, Zhao-An Wang, Ze-Guo Wang +3
Based on a photonic computing platform, we experimentally validate a generalized variational quantum linear solver (VQLS) by systematically solving four-dimensional linear equation…
EPIC-CIM: Training Convolutional Neural Networks on a Coherent Ising Machine via Equilibrium Propagation
Xingrui Yin, Shenwei Kang, Haoqi He +7
Quantum convolutional neural networks, due to the involvement of quantum measurements and discrete quantum state evolution, face inherent training challenges associated with non-di…
Exact and Asymptotically Complete Robust Verifications of Neural Networks via Ising Solvers
Wenxin Li, Wenchao Liu, Anzhe Jiang +6
We present an Ising-compatible framework for formal neural-network robustness verification under bounded input perturbations. For piecewise activations (both continuous piecewise-l…
Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection
Hongdong Zhu, Qi Gao, Yin Ma +6
This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine in…
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…
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…
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…
Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution
Yuhong Huang, Wenxin Li, Chengkang Pan +9
Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better…