Profiling quantum circuits for their efficient execution on single- and multi-core architectures
<|reference_start|>Profiling quantum circuits for their efficient execution on single- and multi-core architectures: Application-specific quantum computers offer the most efficient means to tackle problems intractable by classical computers. Realizing these architectures necessitates a deep understanding of quantum circuit properties and their relationship to execution outcomes on quantum devices. Our study aims to perform for the first time a rigorous examination of quantum circuits by introducing graph theory-based metrics extracted from their qubit interaction graph and gate dependency graph alongside conventional parameters describing the circuit itself. This methodology facilitates a comprehensive analysis and clustering of quantum circuits. Furthermore, it uncovers a connection between parameters rooted in both qubit interaction and gate dependency graphs, and the performance metrics for quantum circuit mapping, across a range of established quantum device and mapping configurations. Among the various device configurations, we particularly emphasize modular (i.e., multi-core) quantum computing architectures due to their high potential as a viable solution for quantum device scalability. This thorough analysis will help us to: i) identify key attributes of quantum circuits that affect the quantum circuit mapping performance metrics; ii) predict the performance on a specific chip for similar circuit structures; iii) determine preferable combinations of mapping techniques and hardware setups for specific circuits; and iv) define representative benchmark sets by clustering similarly structured circuits.<|reference_end|>
@article{bandic2024profiling,
    title={Profiling quantum circuits for their efficient execution on single- and
  multi-core architectures},
    author={Medina Bandic, Pablo le Henaff, Anabel Ovide, Pau Escofet, Sahar Ben
  Rached, Santiago Rodrigo, Hans van Someren, Sergi Abadal, Eduard Alarcon,
  Carmen G. Almudever, Sebastian Feld},
    journal={arXiv preprint arXiv:2407.12640},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12640},
    primaryClass={quant-ph cs.ET cs.LG}
}