activity
20192025
most citedQuantum-Inspired Support Vector Machine

181 citations · 215 across the 13 of their papers we have counts for

collaborators

13 papers

quant-ph2025

Efficient Measurement Error Mitigation with Subsystem-Balanced Pauli Twirling

Xiao-Yue Xu, Chen Ding, Wan-Su Bao

Measurement error mitigation (MEM) is essential for realizing reliable quantum computation. Model-free measurement error mitigation (MF-MEM) is an important class of MEM methods th…

quant-ph2025

Suppressing Measurement Noise in Logical Qubits Through Measurement Scheduling

Xiao-Yue Xu, Chen Ding, Wan-Su Bao

Quantum error correction is essential for reliable quantum computation, where surface codes demonstrate high fault-tolerant thresholds and hardware efficiency. However, noise in si…

quant-ph2025

Physics-inspired Machine Learning for Quantum Error Mitigation

Xiao-Yue Xu, Xin Xue, Tianyu Chen +5

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accur…

quant-ph2024

AI-Powered Algorithm-Centric Quantum Processor Topology Design

Tian Li, Xiao-Yue Xu, Chen Ding +4

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping…

quant-ph2024

JuliVQC: an Efficient Variational Quantum Circuit Simulator for Near-Term Quantum Algorithms

Wei-You Liao, Xiang Wang, Xiao-Yue Xu +4

We introduce JuliVQC: a light-weight, yet extremely efficient variational quantum circuit simulator. JuliVQC is part of an effort for classical simulation of the \textit{Zuchongzhi…

quant-ph2024★ 3 cited

Quantum Advantage: A Single Qubit's Experimental Edge in Classical Data Storage

Chen Ding, Edwin Peter Lobo, Mir Alimuddin +5

We implement an experiment on a photonic quantum processor establishing efficacy of the elementary quantum system in classical information storage. The advantage is established by…

quant-ph2023

Quantum State Compression Shadow

Chen Ding, Xiao-Yue Xu, Shuo Zhang +2

Quantum state readout serves as the cornerstone of quantum information processing, exerting profound influence on quantum communication, computation, and metrology. In this study,…

quant-ph2023★ 1 cited

Circuit-Noise-Resilient Virtual Distillation

Xiao-Yue Xu, Chen Ding, Shuo Zhang +2

Quantum error mitigation (QEM) is vital for improving quantum algorithms' accuracy on noisy near-term devices. A typical QEM method, called Virtual Distillation (VD), can suffer fr…

quant-ph2022★ 9 cited

Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise

Chen Ding, Xiao-Yue Xu, Shuo Zhang +2

As we are entering the era of constructing practical quantum computers, suppressing the inevitable noise to accomplish reliable computational tasks will be the primary goal. Leakag…

quant-ph2022

Active Learning on a Programmable Photonic Quantum Processor

Chen Ding, Xiao-Yue Xu, Yun-Fei Niu +3

Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training…

quant-ph2022★ 7 cited

Parameter-Parallel Distributed Variational Quantum Algorithm

Yun-Fei Niu, Shuo Zhang, Chen Ding +2

Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy intermediate-scale quantum (NISQ) devices. How…

quant-ph2021★ 14 cited

Noise-Resistant Quantum State Compression Readout

Chen Ding, Xiao-Yue Xu, Yun-Fei Niu +2

Qubit measurement is generally the most error-prone operation that degrades the performance of near-term quantum devices, and the exponential decay of readout fidelity severely imp…

cs.LG2019★ 181 cited

Quantum-Inspired Support Vector Machine

Chen Ding, Tian-Yi Bao, He-Liang Huang

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm comp…