10 citations · 10 across the 7 of their papers we have counts for
8 papers
Particle track reconstruction in high-density collider environments with an Evolving-Geometry Transformer
Chenxiu Ou, Yan Wang, Mingjiang Liang +3
Charged particle track reconstruction at the High-Luminosity Large Hadron Collider is challenged by dense hit environments and combinatorial ambiguities. Existing graph-based and T…
SurgeQ: A Hybrid Framework for Ultra-Fast Quantum Processor Design and Crosstalk-Aware Circuit Execution
Xinxuan Chen, Hongxiang Zhu, Zhaohui Yang +4
Executing quantum circuits on superconducting platforms requires balancing the trade-off between gate errors and crosstalk. To address this, we introduce SurgeQ, a hardware-softwar…
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…
CNOT Oriented Synthesis for Small-Scale Boolean Functions Using Spatial Structures of Parallelotopes
Qiang Zheng, Yongzhen Xu, Jiaxi Zhang +2
Quantum computing has garnered significant interest for its potential to achieve exponential speedups over classical approaches. However, in the Noisy Intermediate-Scale Quantum (N…
Super-Robust Nonadiabatic Holonomic Quantum Computation in coherence-protected Superconducting Circuits
Yuan-Sheng Wang, Zhaofeng Su, Xiaosong Chen +1
The schmeme of nonadiabatic holonomic quantum computation (NHQC) offers an error-resistant method for implementing quantum gates, capable of mitigating certain errors. However, the…
SDP for One-shot Dilution of Quantum Coherence
Yikang Zhu, Zhaofeng Su
Quantum coherence is one of the fundamental properties of quantum mechanics and also acts as a valuable resource for a variety of practical applications, which includes quantum com…
VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models
Yanling Lin, Ji Guan, Wang Fang +2
Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noi…
MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework
Xusheng Xu, Jiangyu Cui, Zidong Cui +36
We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) alg…