Profiling quantum circuits for their efficient execution on single- and multi-core architectures
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913434475429888 |
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| author | Bandic, Medina Henaff, Pablo le Ovide, Anabel Escofet, Pau Rached, Sahar Ben Rodrigo, Santiago van Someren, Hans Abadal, Sergi Alarcon, Eduard Almudever, Carmen G. Feld, Sebastian |
| author_facet | Bandic, Medina Henaff, Pablo le Ovide, Anabel Escofet, Pau Rached, Sahar Ben Rodrigo, Santiago van Someren, Hans Abadal, Sergi Alarcon, Eduard Almudever, Carmen G. Feld, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12640 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Profiling quantum circuits for their efficient execution on single- and multi-core architectures Bandic, Medina Henaff, Pablo le Ovide, Anabel Escofet, Pau Rached, Sahar Ben Rodrigo, Santiago van Someren, Hans Abadal, Sergi Alarcon, Eduard Almudever, Carmen G. Feld, Sebastian Quantum Physics Emerging Technologies Machine Learning 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. |
| title | Profiling quantum circuits for their efficient execution on single- and multi-core architectures |
| topic | Quantum Physics Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2407.12640 |