95 citations · 137 across the 12 of their papers we have counts for
13 papers
Learnable yet not simulable: a quantum resource theory of learning models
Xinbiao Wang, Yuxuan Du, Dacheng Tao
Quantum resource theory has sharpened our understanding of the intrinsic complexity of quantum systems, particularly their classical simulability. However, it remains unclear which…
Stochastic Pauli-path simulator for large-scale quantum optimization
Kaining Zhang, Xinbiao Wang, Kunsheng Li +4
Pauli-based simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime. Yet their applicability remains largely limited to f…
AQER: a scalable and efficient data loader for digital quantum computers
Kaining Zhang, Xinbiao Wang, Yuxuan Du +2
Digital quantum computing promises to offer computational capabilities beyond the reach of classical systems, yet its capabilities are often challenged by scarce quantum resources.…
AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Xinbiao Wang, Yuxuan Du, Zihan Lou +5
Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learn…
Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Wei-You Liao, Yuxuan Du, Xinbiao Wang +5
The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum proces…
SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs
Shuhan Xu, Siyuan Liang, Hongling Zheng +6
Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject…
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo +6
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum compute…
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
Yang Qian, Xinbiao Wang, Yuxuan Du +2
Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization…
Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem
Xinbiao Wang, Yuxuan Du, Kecheng Liu +3
The No-Free-Lunch (NFL) theorem, which quantifies problem- and data-independent generalization errors regardless of the optimization process, provides a foundational framework for…
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…
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…
The dilemma of quantum neural networks
Yang Qian, Xinbiao Wang, Yuxuan Du +2
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliab…
Towards understanding the power of quantum kernels in the NISQ era
Xinbiao Wang, Yuxuan Du, Yong Luo +1
A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can a…