AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs

Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum hardware. However, the non-local quantum communication between quantum nodes is much more expensive and error-prone than the local quantum operation within each quantum device. Previ...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) s. 1027 - 1041
Hlavní autoři: Wu, Anbang, Zhang, Hezi, Li, Gushu, Shabani, Alireza, Xie, Yuan, Ding, Yufei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2022
Témata:
Costs
Distributed computing
Hardware
Logic gates
Microarchitecture
Program processors
quantum compiler
Quantum computing
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum hardware. However, the non-local quantum communication between quantum nodes is much more expensive and error-prone than the local quantum operation within each quantum device. Previous DQC compilers focus on optimizing the implementation of each non-local gate and adopt similar compilation designs to single-node quantum compilers. The communication patterns in distributed quantum programs remain unexplored, leading to a far-from-optimal communication cost. In this paper, we identify burst communication, a specific qubit-node communication pattern that widely exists in various distributed quantum programs and can be leveraged to guide communication overhead optimization. We then propose AutoComm, an automatic compiler framework to extract burst communication patterns from input programs and then optimize the communication steps of burst communication discovered. Compared to state-of-the-art DQC compilers, experimental results show that our proposed AutoComm can reduce the communication resource consumption and the program latency by 72.9% and 69.2% on average, respectively.
DOI:10.1109/MICRO56248.2022.00074