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Quanxi Li

dblp:16/4289 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Parallel and multicore computing › concurrent programming
asynchronous execution
0.912025
Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025
Memory systems
memory disaggregation
0.912025
Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025
Processor architecture and microarchitecture › multithreading
multithreaded execution
0.912025
Beehive: A Scalable Disaggregated Memory Runtime Exploiting Asynchrony of Multithreaded Programs · NSDI 2025
Hardware reliability and fault tolerance › error detection
silent data corruption detection
0.912025
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
0.412020
Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices · DAC 2020
Emerging computing paradigms › quantum computer architecture
qubit mapping
0.412020
Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices · DAC 2020
Compilers and program optimization › domain-specific compilation
quantum compilation
0.112020
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
YearPublicationVenuePosition
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
NSDI1
2025 Orthrus: Efficient and Timely Detection of Silent User Data Corruption in the Cloud with Resource-Adaptive Computation Validation
abstract
Even 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
SOSP3
2020 Codar: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices
abstract
Quantum 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
DAC3
2019 Optimizing Quantum Programs Against Decoherence: Delaying Qubits into Quantum Superposition
abstract
Quantum 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
TASE3