collaborators

20 papers

quant-ph2026

Simulation of Lindbladian dynamics via adaptive variational quantum trajectory compression

Huan-Yu Liu, Cheng Xue, Yun-Jie Wang +5

Quantum simulation of open quantum systems in the noisy intermediate-scale quantum (NISQ) era is hindered by the non-unitary nature of dissipative dynamics and the limited quantum…

quant-ph2026

Routing Codes: High-Rate Quantum LDPC Codes with Short, Parallel Non-Local Connectivity

Jiaxuan Zhang, Zhao-Yun Chen, Peng Duan +6

Quantum low-density parity-check (qLDPC) codes are promising candidates for realizing large-scale fault-tolerant quantum computing. Although many codes with favorable theoretical p…

cs.AI2026

QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

Songxin Qu, Tai-Ping Sun, Yun-Jie Wang +8

Large language models (LLMs) show strong capabilities in general reasoning but typically lack reliability in scientific domains like quantum mechanics, which demand strict adherenc…

quant-ph2026

Adaptive Deformation of Color Code in Square Lattices with Defects

Tian-Hao Wei, Jia-Xuan Zhang, Jia-Ning Li +3

Quantum error correction is a crucial technology for fault tolerant quantum computing. On superconducting platforms, hardware defects in large scale quantum processors can disrupt…

quant-ph2026

Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation

Shengbin Wang, Guihui Li, Zhimin Wang +5

Solving combinatorial optimization problems using variational quantum algorithms (VQAs) might be a promise application in the NISQ era. However, the limited trainability of VQAs co…

quant-ph2026

Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor

Hai-Feng Zhang, Zhao-Yun Chen, Peng Wang +17

Quantum machine learning (QML) models, like their classical counterparts, are vulnerable to adversarial attacks, hindering their secure deployment. Here, we report the first system…