193 citations · 386 across the 31 of their papers we have counts for
32 papers
Multimodal deep representation learning for quantum cross-platform verification
Yang Qian, Yuxuan Du, Zhenliang He +2
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executi…
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
ShadowNet for Data-Centric Quantum System Learning
Yuxuan Du, Yibo Yang, Tongliang Liu +3
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality. Statistical learning offers new possibilities in this regime through neural network…
Understanding Deep Neural Networks via Linear Separability of Hidden Layers
Chao Zhang, Xinyu Chen, Wensheng Li +3
In this paper, we measure the linear separability of hidden layer outputs to study the characteristics of deep neural networks. In particular, we first propose Minkowski difference…
Transition Role of Entangled Data in Quantum Machine Learning
Xinbiao Wang, Yuxuan Du, Zhuozhuo Tu +3
Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entan…
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Jiarui Feng, Lecheng Kong, Hao Liu +4
Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by th…
TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework
Yaocheng Chen, Chung-Yun Kuo, Yuxuan Du +2
TeD-Q is an open-source software framework for quantum machine learning, variational quantum algorithm (VQA), and simulation of quantum computing. It seamlessly integrates classica…
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Yuxuan Du, Yibo Yang, Dacheng Tao +1
Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Some QNNs with spe…
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
Hao Wang, Lixue Liu, Xueguan Song +2
In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e.g.} surface…
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations
Yuanyuan Wang, Wei Huang, Mingming Gong +4
Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. However, the theoretical aspects, e.g., identifiability and asymptotic propertie…
Alternating Differentiation for Optimization Layers
Haixiang Sun, Ye Shi, Jingya Wang +3
The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existin…
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
Yang Qian, Yuxuan Du, Dacheng Tao
The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical a…
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Aishan Liu, Shiyu Tang, Siyuan Liang +4
Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization…
Symmetric Pruning in Quantum Neural Networks
Xinbiao Wang, Junyu Liu, Tongliang Liu +3
Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance of ground states preparation (GSP), this task is classica…
Balancing Stability and Plasticity through Advanced Null Space in Continual Learning
Yajing Kong, Liu Liu, Zhen Wang +1
Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy…
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
Jixian Guo, Mingming Gong, Dacheng Tao
The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods tr…
Recent Advances for Quantum Neural Networks in Generative Learning
Jinkai Tian, Xiaoyu Sun, Yuxuan Du +11
Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is thr…
QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines
Zeqiao Zhou, Yuxuan Du, Xinmei Tian +1
The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimizatio…
Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method
Wensheng Li, Chao Zhang, Chuncheng Wang +2
Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of…
Power of Quantum Generative Learning
Yuxuan Du, Zhuozhuo Tu, Bujiao Wu +2
The intrinsic probabilistic nature of quantum mechanics invokes endeavors of designing quantum generative learning models (QGLMs). Despite the empirical achievements, the foundatio…
BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
Zhi Hou, Baosheng Yu, Dacheng Tao
Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribu…
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation
Robin Wang, Yibo Yang, Dacheng Tao
Point cloud classifiers with rotation robustness have been widely discussed in the 3D deep learning community. Most proposed methods either use rotation invariant descriptors as in…
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Kaining Zhang, Liu Liu, Min-Hsiu Hsieh +1
Variational quantum circuits have been widely employed in quantum simulation and quantum machine learning in recent years. However, quantum circuits with random structures have poo…
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Yibo Yang, Shixiang Chen, Xiangtai Li +3
Modern deep neural networks for classification usually jointly learn a backbone for representation and a linear classifier to output the logit of each class. A recent study has sho…