9 papers
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…
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…
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…
Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space
Kyurae Kim, Qiang Fu, Yi-An Ma +2
For approximating a target distribution given only its unnormalized log-density, stochastic gradient-based variational inference (VI) algorithms are a popular approach. For example…
A versatile coherent Ising computing platform
Hai Wei, Chengjun Ai, Putuo Guo +12
Coherent Ising Machines (CIMs) have emerged as a hybrid form of quantum computing devices designed to solve NP-complete problems, offering an exciting opportunity for discovering o…
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…
Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design
Yinhui Ma, Tomomasa Yamasaki, Zhehui Wang +2
Hardware-Aware Neural Architecture Search (HW-NAS) is an efficient approach to automatically co-optimizing neural network performance and hardware energy efficiency, making it part…
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…