ASPLOS'23:
Exploiting the Regular Structure of Modern Quantum Architectures for Compiling and Optimizing Programs with Permutable Operators
Yuwei Jin, Fei Hua, Yanhao Chen, Ari Hayes, C. Zhang, Eddy Z. Zhang
The 28th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2023), Vancouver, March 2023
This paper explores optimization techniques for quantum programs with permutable operators, leveraging the regular structure of modern quantum architectures.
MICRO'21:
AutoBraid: A Framework for Enabling Efficient Surface Communication in Quantum Computing
F. Hua, Y. Chen, Y. Jin, C. Zhang, A.B. Hayes, Y. Zhang, E.Z. Zhang.
The 54th IEEE/ACM International Symposium on Microarchitecture (MICRO), Virtual, October 2021.
AutoBraid presents a framework for optimizing surface communication in quantum computing systems, addressing key challenges in quantum architecture design.
ASPLOS'21:
C. Zhang, A.B. Hayes, L. Qiu, Y. Jin, Y. Chen, E.Z. Zhang.
The 26th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2021), Virtual, April 2021.
This paper introduces a novel approach to qubit mapping that optimizes execution time, a critical factor in the NISQ era of quantum computing.
arxiv:
C. Zhang, Y. Chen, Y. Jin, W. Ahn, Y. Zhang, E.Z. Zhang.
arXiv preprint arXiv:2009.02346 (2020)
SlackQ introduces a slack-aware approach to the qubit mapping problem, improving efficiency in quantum circuit compilation.
DAC'20-WIP:
C. Zhang, Y. Chen, Y. Jin, W. Ahn, Y. Zhang, E.Z. Zhang.
The 57th ACM/IEEE Design Automation Conference (DAC), Work in Progress Poster session, San Francisco, July 2020.
This work presents a depth-aware approach to swap insertion for qubit mapping, addressing key challenges in quantum circuit compilation.
arxiv:
Y. Chen, F. Hua, C. Huang, J. Bierema, C. Zhang, E.Z. Zhang.
arXiv preprint arXiv:1906.06504 (2019)
This paper explores techniques for accelerating concurrent heap operations on GPU architectures, addressing performance challenges in parallel computing.
ATC'18:
Y. Chen, A. Hayes, C. Zhang, T. Salmon, E.Z. Zhang.
Proceedings of the USENIX Annual Technical Conference (USENIX ATC 2018), Boston, MA, July 2018.
This work introduces locality-aware software throttling techniques to optimize sparse matrix operations on GPUs, improving performance for data-intensive applications.
NVMSA'16:
C.Zhang, W.Ahn, Y.Zhang, B.Childers.
Non-Volatile Memory Systems and Applications Symposium (NVMSA), 2016 5th. IEEE, 2016.
This paper addresses the challenges of live code updates for IoT devices in energy-constrained environments, proposing novel techniques for efficient updates.