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Yuchuan Hu

dblp:339/5542 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2025
0000-0002-2117-3328ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Computer networks · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms
quantum computer architecture
0.812024
A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024
Emerging computing paradigms › quantum computer architecture
qubit mapping
0.812024
A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024
Emerging computing paradigms › quantum computing
NISQ
0.212024
A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping · IEEE Trans. Computers 2024

Methods — techniques the papers use, named apart from their topics

mutual-influence-aware heuristic · 0.8iterated local search · 0.8
YearPublicationVenuePosition
2025 SDDP: sensitive data detection method for user-controlled data pricing
Yuchuan Hu, Bitao Hu, Bing Guo 0003, Cheng Dai, Yan Shen 0001
Appl. Intell.1
2025 A defense mechanism for federated learning in AIoT through critical gradient dimension extraction
Bing Guo 0003, Yan Shen 0001, Shengxin Dai, Cheng Dai, Yuchuan Hu
Comput. Commun.7
2024 A Mutual-Influence-Aware Heuristic Method for Quantum Circuit Mapping
abstract
Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.
Kui Ye, Shengxin Dai, Bing Guo 0003, Yan Shen 0001, Chuanjie Liu, Kejun Bi, Yuchuan Hu, Mingjie Zhao 0002
IEEE Trans. Computers8