TY - GEN
T1 - SupermarQ
T2 - 28th Annual IEEE International Symposium on High-Performance Computer Architecture, HPCA 2022
AU - Tomesh, Teague
AU - Gokhale, Pranav
AU - Omole, Victory
AU - Ravi, Gokul Subramanian
AU - Smith, Kaitlin N.
AU - Viszlai, Joshua
AU - Wu, Xin Chuan
AU - Hardavellas, Nikos
AU - Martonosi, Margaret R.
AU - Chong, Frederic T.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.
AB - The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.
KW - Benchmarking
KW - Program Characterization
KW - Quantum Computing
UR - http://www.scopus.com/inward/record.url?scp=85130761311&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130761311&partnerID=8YFLogxK
U2 - 10.1109/HPCA53966.2022.00050
DO - 10.1109/HPCA53966.2022.00050
M3 - Conference contribution
AN - SCOPUS:85130761311
T3 - Proceedings - International Symposium on High-Performance Computer Architecture
SP - 587
EP - 603
BT - Proceedings - 2022 IEEE International Symposium on High-Performance Computer Architecture, HPCA 2022
PB - IEEE Computer Society
Y2 - 2 April 2022 through 6 April 2022
ER -