Demo Data from VLDB 2026 / reviewers in the wild / expert
Yu-Chun Wu
dblp:279/4837
· DBLP profile ↗
5ranked-venue papers
0as first author
5since 2021 · last 2026
0000-0002-8997-3030ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
3 papers |
Quantum computing and quantum information · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Emerging computing paradigms · 91% Electronic design automation · 9% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Computational finance and economics · 100% |
Topics — the 8 heaviest of 10, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › complex reasoning
scientific reasoning |
1.0 | 1 | 2026 | QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning · ACL (1) 2026 |
Emerging computing paradigms › quantum computer architecture
quantum compilation |
0.9 | 1 | 2025 | CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025 |
Emerging computing paradigms
quantum computer architecture |
0.9 | 1 | 2025 | CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025 |
Emerging computing paradigms › quantum computer architecture
qubit mapping and scheduling |
0.9 | 1 | 2025 | CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025 |
Quantum computing and quantum information
quantum algorithms |
0.9 | 1 | 2025 | Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance · Sci. China Inf. Sci. 2025 |
Quantum computing and quantum information › quantum algorithms
variational quantum eigensolver |
0.9 | 1 | 2025 | Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance · Sci. China Inf. Sci. 2025 |
Computational finance and economics › portfolio management
portfolio optimization |
0.3 | 1 | 2025 | Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance · Sci. China Inf. Sci. 2025 |
Electronic design automation › signal integrity
crosstalk mitigation |
0.3 | 1 | 2025 | CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025 |
Methods — techniques the papers use, named apart from their topics
verification-aware training · 2.0reinforcement learning · 2.0problem-inspired ansatz · 1.7pulse compensation · 0.9gate scheduling · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement LearningabstractSongxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Cheng Xue, Xiao-Fan Xu, Han Fang, Yang Yang, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Songxin Qu, Tai-Ping Sun, Yun-Jie Wang, Huan-Yu Liu, Xiao-Fan Xu, Yang Yang 0026, Yu-Chun Wu, Guo-Ping Guo, Zhao-Yun Chen |
ACL (1) | 9 |
| 2026 | HiMA: Hierarchical quantum microarchitecture for qubit-scaling and quantum process-level parallelism
Zi-Hao Mei, Han-Qing Shi, Liang-Liang Guo, Xiao-Yan Yang, Yun-Jie Wang, Xiao-Fan Xu, Cheng Xue 0006, Wei-Cheng Kong, Jun-Chao Wang, Yu-Chun Wu, Zhao-Yun Chen, Guo-Ping Guo |
Future Gener. Comput. Syst. | 11 |
| 2025 | Quantum computational insurance and actuarial science
Huan-Yu Liu, Xi-Ning Zhuang, Qing-Song Li, Menghan Dou, Zhao-Yun Chen, Yu-Chun Wu, Guo-Ping Guo, Guang-Can Guo |
Sci. China Inf. Sci. | 8 |
| 2025 | Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance
Shengbin Wang, Guihui Li, ShuBin Zhao, Dongyi Zhao, Menghan Dou, Yongjian Gu, Yu-Chun Wu, Guoping Guo |
Sci. China Inf. Sci. | 10 |
| 2025 | CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum ChipsabstractCrosstalk poses a significant challenge in quantum computing, particularly when quantum gates are executed in parallel, as qubit frequency resonance can lead to residual coupling and reduced gate fidelity. Current solutions struggle to mitigate both crosstalk and decoherence during parallel two-qubit gate operations on frequency-tunable quantum chips. To address this, we propose a crosstalk-aware mapping and gate scheduling (CAMEL) approach, designed to mitigate crosstalk and suppress decoherence by leveraging the tunable coupler’s physical properties and incorporating a pulse compensation technique. CAMEL operates within a two-step compilation framework: first, a qubit mapping strategy that considers both crosstalk and decoherence; and second, a gate timing scheduling method that prioritizes the execution of the largest possible set of crosstalk-free parallel gates, reducing overall circuit execution time. Evaluation results demonstrate CAMEL’s superior ability to mitigate crosstalk compared to crosstalk-agnostic methods, while successfully suppressing decoherence where other approaches fail. Additionally, CAMEL performs better than dynamic-frequency-aware techniques, particularly in low-complexity hardware environments. Bin-Han Lu, Zhao-Yun Chen, Huan-Yu Liu, Tai-Ping Sun, Yu-Chun Wu, Guo-Ping Guo |
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. | 7 |