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20192023
most citedQuantum circuit architecture search for variational quantum algorithms

193 citations · 386 across the 31 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

quant-ph2023

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…

cs.LG2023★ 13 cited

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…

quant-ph2023

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…

cs.LG2023★ 1 cited

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…

quant-ph2023★ 19 cited

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…

cs.LG2023

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…

quant-ph2023

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…

quant-ph2023

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…