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Qubit allocation as a combination of subgraph isomorphism and token swapping

Authors:

Marcos Yukio Siraichi,

Vinícius Fernandes dos Santos,

Caroline Collange,

Fernando Magno Quintão PereiraAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 3, Issue OOPSLA

Article No.: 120, Pages 1 - 29

https://doi.org/10.1145/3360546

Published: 10 October 2019 Publication History

Abstract

In 2016, the first quantum processors have been made available to the general public. The possibility of programming an actual quantum device has elicited much enthusiasm. Yet, such possibility also brought challenges. One challenge is the so called Qubit Allocation problem: the mapping of a virtual quantum circuit into an actual quantum architecture. There exist solutions to this problem; however, in our opinion, they fail to capitalize on decades of improvements on graph theory. In contrast, this paper shows how to model qubit allocation as the combination of Subgraph Isomorphism and Token Swapping. This idea has been made possible by the publication of an approximative solution to the latter problem in 2016. We have compared our algorithm against five other qubit allocators, all independently designed in the last two years, including the winner of the IBM Challenge. When evaluated in "Tokyo", a quantum architecture with 20 qubits, our technique outperforms these state-of-the-art approaches in terms of the quality of the solutions that it finds and the amount of memory that it uses, while showing practical runtime.

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Qubit allocation as a combination of subgraph isomorphism and token swapping

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cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 3, Issue OOPSLA

October 2019

2077 pages

EISSN:2475-1421

DOI:10.1145/3366395

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Copyright © 2019 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

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Published: 10 October 2019

Published in PACMPL Volume 3, Issue OOPSLA

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Cited By

Guo ZWang THui-Ru Jiang IPosser G(2024)SMT-Based Layout Synthesis Approaches for Quantum CircuitsProceedings of the 2024 International Symposium on Physical Design10.1145/3626184.3633316(235-243)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1145/3626184.3633316
Heng SKim DHan Y(2024)Mitigating fabrication errors by recovering defective syndrome qubits in surface codePhysical Review A10.1103/PhysRevA.109.012420109:1Online publication date: 16-Jan-2024
https://doi.org/10.1103/PhysRevA.109.012420
Li YLiu WLi M(2024)Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor ArchitecturesAdvanced Quantum Technologies10.1002/qute.2023002897:2Online publication date: 2-Jan-2024
https://doi.org/10.1002/qute.202300289
MATSUO AHATTORI WYAMASHITA S(2023)An Efficient Method to Decompose and Map MPMCT Gates That Accounts for Qubit PlacementIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2022EAP1050E106.A:2(124-132)Online publication date: 1-Feb-2023
https://doi.org/10.1587/transfun.2022EAP1050
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Hua FJin YChen YVittal SKrsulich KBishop LLapeyre JJavadi-Abhari AZhang EAamodt TJerger NSwift M(2023)CaQR: A Compiler-Assisted Approach for Qubit Reuse through Dynamic CircuitProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 310.1145/3582016.3582030(59-71)Online publication date: 25-Mar-2023
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Paler ASasu LFlorea AAndonie R(2023)Machine Learning Optimization of Quantum Circuit LayoutsACM Transactions on Quantum Computing10.1145/35652714:2(1-25)Online publication date: 24-Feb-2023
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