314 citations · 1.4k across the 44 of their papers we have counts for
47 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…
Learning to Reconstruct Wigner Functions in Phase Space
Xinyu Tang, Yi-hsin Lin, Yan Zhu +5
Wigner function learning is a central tool for characterizing continuous variable quantum systems. A fundamental challenge in this setting is to infer a continuous phase-space func…
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.…
Endowing Molecular Language with Geometry Perception via Modality Compensation for High-Throughput Quantum Hamiltonian Prediction
Zhenzhong Wang, Yongjie Hou, Chenggong Huang +3
The quantum Hamiltonian is a fundamental property that governs a molecule's electronic structure and behavior, and its calculation and prediction are paramount in computational che…
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…
Artificial intelligence for representing and characterizing quantum systems
Yuxuan Du, Yan Zhu, Yuan-Hang Zhang +8
Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quant…
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…
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…
Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits
Yuxuan Du, Min-Hsiu Hsieh, Dacheng Tao
The vast and complicated large-qubit state space forbids us to comprehensively capture the dynamics of modern quantum computers via classical simulations or quantum tomography. Rec…
Quantum Compiling with Reinforcement Learning on a Superconducting Processor
Z. T. Wang, Qiuhao Chen, Yuxuan Du +16
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a…
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…
Optical Quantum Sensing for Agnostic Environments via Deep Learning
Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin +3
Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge…
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…
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…
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…
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…
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…
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…
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…
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
Yingbin Bai, Erkun Yang, Zhaoqing Wang +5
Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augm…
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…