Demo Data from VLDB 2026 / reviewers in the wild / expert
Quanxi Li
dblp:16/4289
· DBLP profile ↗
4ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0000-1191-8675ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 since 2021Systems, architecture and hardware · 1Computer networks · 1 · 1 first-author · 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
3 papers |
Hardware reliability and fault tolerance · 29% Emerging computing paradigms · 14% Memory systems · 14% | |
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% |
Topics — the 7 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › concurrent programming
asynchronous execution |
0.9 | 1 | 2025 | Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025 |
Memory systems
memory disaggregation |
0.9 | 1 | 2025 | Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025 |
Processor architecture and microarchitecture › multithreading
multithreaded execution |
0.9 | 1 | 2025 | Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025 |
Hardware reliability and fault tolerance › error detection
silent data corruption detection |
0.9 | 1 | 2025 | Orthrus: Efficient and Timely Detection of Silent User Data Corruption in the Cloud with Resource-Adaptive Computation Validation · SOSP 2025 |
Emerging computing paradigms
quantum computer architecture |
0.4 | 1 | 2020 | Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices · DAC 2020 |
Emerging computing paradigms › quantum computer architecture
qubit mapping |
0.4 | 1 | 2020 | Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices · DAC 2020 |
Compilers and program optimization › domain-specific compilation
quantum compilation |
0.1 | 1 | 2020 | Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices · DAC 2020 |
Methods — techniques the papers use, named apart from their topics
error injection · 0.9compiler support · 0.9asynchronous computation validation · 0.9maQAM · 0.9duration-aware remapping · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs
Quanxi Li, Ying Liu 0055, Yanwen Xia, Jie Zhang 0048, Mosong Zhou, Xiaobing Feng 0002, Huimin Cui, Yizhou Shan, Chenxi Wang 0005 |
NSDI | 1 |
| 2025 | Orthrus: Efficient and Timely Detection of Silent User Data Corruption in the Cloud with Resource-Adaptive Computation ValidationabstractEven with substantial endeavors to test and validate processors, computational errors may still arise post-installation. One particular category of CPU errors transpires discreetly, without crashing applications or triggering hardware warnings. These elusive errors pose a significant threat by undermining user data, and their detection is challenging. This paper introduces Orthrus, a solution for the timely detection of silent user data corruption caused by post-installation CPU errors. Orthrus safeguards user data in cloud applications by providing simple annotations and compiler support for users to identify data operators and validating these operators asynchronously across cores while maintaining a low overhead (2%–6%), making it practical for production deployment. Our evaluation, using carefully injected errors, demonstrates that Orthrus can detect 87% of data corruptions with just a single core dedicated to validation, increasing to 91% and 96% when two and four cores are used, respectively. Chenxiao Liu, Zhenting Zhu, Quanxi Li, Yanwen Xia, Yifan Qiao 0002, Xiangyun Deng, Youyou Lu, Tao Xie 0001, Huimin Cui, Zidong Du, Guoqing Harry Xu, Chenxi Wang 0005 |
SOSP | 3 |
| 2020 | Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ DevicesabstractQuantum computing devices in the NISQ era share common features and challenges like limited connectivity between qubits. Since two-qubit gates are allowed on limited qubit pairs, quantum compilers must transform original quantum programs to fit the hardware constraints. Previous works on qubit mapping assume different gates have the same execution duration, which limits them to explore the parallelism from the program. To address this drawback, we propose a Multi-architecture Adaptive Quantum Abstract Machine (maQAM) and a COntext-sensitive and Duration-Aware Remapping algorithm (Codar). The Codar remapper is aware of gate duration difference and program context, enabling it to extract more parallelism from programs and speed up the quantum programs by 1.23 in simulation on average in different architectures and maintain the fidelity of circuits when running on OriginQ quantum noisy simulator. Haowei Deng, Yu Zhang 0086, Quanxi Li |
DAC | 3 |
| 2019 | Optimizing Quantum Programs Against Decoherence: Delaying Qubits into Quantum SuperpositionabstractQuantum computing technology has reached a second renaissance in the last decade. However, in the NISQ era pointed out by John Preskill in 2018, quantum noise and decoherence, which affect the accuracy and execution effect of quantum programs, cannot be ignored and corrected by the near future NISQ computers. In order to let users more easily write quantum programs, the compiler and runtime system should consider underlying quantum hardware features such as decoherence. To address the challenges posed by decoherence, in this paper, we propose and prototype QLifeReducer to minimize the qubit lifetime in the input OpenQASM program by delaying qubits into quantum superposition. QLifeReducer includes three core modules, i.e., the parser, parallelism analyzer and transformer. It introduces the layered bundle format to express the quantum program, where a set of parallelizable quantum operations is packaged into a bundle. We evaluate quantum programs before and after transformed by QLifeReducer on both real IBM Q 5 Tenerife and the self-developed simulator. The experimental results show that QLifeReducer reduces the error rate of a quantum program when executed on IBMQ 5 Tenerife by 11%; and can reduce the longest qubit lifetime as well as average qubit lifetime by more than 20% on most quantum workloads. Yu Zhang 0086, Haowei Deng, Quanxi Li, Haoze Song, Leihai Nie |
TASE | 3 |