TY - GEN
T1 - AltGraph
T2 - 34th Great Lakes Symposium on VLSI 2024, GLSVLSI 2024
AU - Beaudoin, Collin
AU - Phalak, Koustubh
AU - Ghosh, Swaroop
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/6/12
Y1 - 2024/6/12
N2 - Quantum circuit transformation aims to optimize circuits for depth, gate count, and compatibility with Noisy Intermediate Scale Quantum (NISQ) devices that suffer from various error sources. Prior methods use combinations of expert-defined rules and Reinforcement Learning (RL). We introduce AltGraph, a novel approach employing generative graph models to generate functionally equivalent quantum circuits using - specifically, Direct Acyclic Graph (DAG) Variational Autoencoder (D-VAE) variants (GRU and GCN) and Deep Generative Model for Graphs (DeepGMG). AltGraph perturbs the latent space to generate quantum circuits optimized for hardware coupling maps, reducing gate count by 37.55% and circuit depth by 37.75% post-transpiling, with 0.0074 Mean Squared Error (MSE) in the density matrix - outperforming state-of-the-art methods by 2.56%.
AB - Quantum circuit transformation aims to optimize circuits for depth, gate count, and compatibility with Noisy Intermediate Scale Quantum (NISQ) devices that suffer from various error sources. Prior methods use combinations of expert-defined rules and Reinforcement Learning (RL). We introduce AltGraph, a novel approach employing generative graph models to generate functionally equivalent quantum circuits using - specifically, Direct Acyclic Graph (DAG) Variational Autoencoder (D-VAE) variants (GRU and GCN) and Deep Generative Model for Graphs (DeepGMG). AltGraph perturbs the latent space to generate quantum circuits optimized for hardware coupling maps, reducing gate count by 37.55% and circuit depth by 37.75% post-transpiling, with 0.0074 Mean Squared Error (MSE) in the density matrix - outperforming state-of-the-art methods by 2.56%.
UR - http://www.scopus.com/inward/record.url?scp=85197868848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85197868848&partnerID=8YFLogxK
U2 - 10.1145/3649476.3658747
DO - 10.1145/3649476.3658747
M3 - Conference contribution
AN - SCOPUS:85197868848
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 44
EP - 49
BT - GLSVLSI 2024 - Proceedings of the Great Lakes Symposium on VLSI 2024
PB - Association for Computing Machinery
Y2 - 12 June 2024 through 14 June 2024
ER -