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Tai-Ping Sun

dblp:29/3031 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

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.

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%
Theoretical computer science
1 paper
Quantum computing and quantum information · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › complex reasoning
scientific reasoning
1.012026
QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning · ACL (1) 2026
Emerging computing paradigms › quantum computer architecture
quantum compilation
0.912025
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
0.912025
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
0.912025
CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2025
Electronic design automation › signal integrity
crosstalk mitigation
0.312025
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 · 2.0pulse compensation · 0.9gate scheduling · 0.9
YearPublicationVenuePosition
2026 QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning
abstract
Songxin 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)2
2025 CAMEL: Physically Inspired Crosstalk-Aware Mapping and Gate Scheduling for Frequency-Tunable Quantum Chips
abstract
Crosstalk 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.5
1997 Focal-plane-arrays and CMOS readout techniques of infrared imaging systems
abstract
A discussion of CMOS readout technologies for infrared (IR) imaging systems is presented. First, the description of various types of IR detector materials and structures is given. The advances of detector fabrication technology and microelectronics process technology have led to the development of large format array of IR imaging detectors. For such large IR FPAs which is the critical component of the advanced infrared imaging system, general requirement and specifications are described. To support a good interface between the FPA and downstream signal processing stage, both conventional and CMOS readout techniques are presented and discussed. Finally, future development directions including the smart focal plane concept are also introduced.
Chih-Cheng Hsieh, Chung-Yu Wu, Far-Wen Jih, Tai-Ping Sun
IEEE Trans. Circuits Syst. Video Technol.4