9 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…
Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
Haipeng Cao, Kaining Zhang, Dacheng Tao +1
Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data. Recently, inspired by classical diffusi…
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…