The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Baru-baru ini, Quantum Annealing (QA) telah menarik perhatian sebagai algoritma yang cekap untuk masalah pengoptimuman gabungan. Dalam QA, saiz data input menjadi besar dan pengurangannya adalah penting untuk mempercepatkan oleh emulasi perkakasan kerana saiz memori yang boleh digunakan dan lebar jalurnya adalah terhad. Makalah ini mencadangkan kaedah mampatan input matriks jarang untuk emulator QA. Kaedah yang dicadangkan menggunakan jarang matriks pekali dan kemunculan semula nilai yang sama. Jadual bebas diperkenalkan dan data dimampatkan dengan kaedah carian dan pendaftaran dua data berturut-turut dalam jadual nilai. Kaedah yang dicadangkan digunakan untuk Travelling Salesman Problem (TSP) dengan 32, 64 dan 96 bandar serta Nurse Scheduling Problem (NSP). Kaedah yang dicadangkan boleh mengurangkan jumlah data sebanyak 1/40 untuk 96 TSP bandar dan boleh menguruskan 96 TSP bandar pada emulator perkakasan. Apabila digunakan pada NSP, kami mengesahkan keberkesanan kaedah yang dicadangkan dengan nisbah mampatan antara 1/4 hingga 1/11.8. Pengurangan data juga berguna untuk prestasi simulasi/emulasi apabila menggunakan data mampat secara langsung dan kelajuan 1.9 kali lebih pantas boleh didapati di 96 bandar TSP berbanding kaedah berasaskan CSR.
Sohei SHIMOMAI
Waseda University
Kei UEDA
Fujitsu
Shinji KIMURA
Waseda University
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Salinan
Sohei SHIMOMAI, Kei UEDA, Shinji KIMURA, "Input Data Format for Sparse Matrix in Quantum Annealing Emulator" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 3, pp. 557-565, March 2024, doi: 10.1587/transfun.2023VLP0002.
Abstract: Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for accelerating by the hardware emulation since the usable memory size and its bandwidth are limited. The paper proposes the compression method of input sparse matrices for QA emulator. The proposed method uses the sparseness of the coefficient matrix and the reappearance of the same values. An independent table is introduced and data are compressed by the search and registration method of two consecutive data in the value table. The proposed method is applied to Traveling Salesman Problem (TSP) with 32, 64 and 96 cities and Nurse Scheduling Problem (NSP). The proposed method could reduce the amount of data by 1/40 for 96 city TSP and could manage 96 city TSP on the hardware emulator. When applied to NSP, we confirmed the effectiveness of the proposed method by the compression ratio ranging from 1/4 to 1/11.8. The data reduction is also useful for the simulation/emulation performance when using the compressed data directly and 1.9 times faster speed can be found on 96 city TSP than the CSR-based method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023VLP0002/_p
Salinan
@ARTICLE{e107-a_3_557,
author={Sohei SHIMOMAI, Kei UEDA, Shinji KIMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Input Data Format for Sparse Matrix in Quantum Annealing Emulator},
year={2024},
volume={E107-A},
number={3},
pages={557-565},
abstract={Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for accelerating by the hardware emulation since the usable memory size and its bandwidth are limited. The paper proposes the compression method of input sparse matrices for QA emulator. The proposed method uses the sparseness of the coefficient matrix and the reappearance of the same values. An independent table is introduced and data are compressed by the search and registration method of two consecutive data in the value table. The proposed method is applied to Traveling Salesman Problem (TSP) with 32, 64 and 96 cities and Nurse Scheduling Problem (NSP). The proposed method could reduce the amount of data by 1/40 for 96 city TSP and could manage 96 city TSP on the hardware emulator. When applied to NSP, we confirmed the effectiveness of the proposed method by the compression ratio ranging from 1/4 to 1/11.8. The data reduction is also useful for the simulation/emulation performance when using the compressed data directly and 1.9 times faster speed can be found on 96 city TSP than the CSR-based method.},
keywords={},
doi={10.1587/transfun.2023VLP0002},
ISSN={1745-1337},
month={March},}
Salinan
TY - JOUR
TI - Input Data Format for Sparse Matrix in Quantum Annealing Emulator
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 557
EP - 565
AU - Sohei SHIMOMAI
AU - Kei UEDA
AU - Shinji KIMURA
PY - 2024
DO - 10.1587/transfun.2023VLP0002
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2024
AB - Recently, Quantum Annealing (QA) has attracted attention as an efficient algorithm for combinatorial optimization problems. In QA, the input data size becomes large and its reduction is important for accelerating by the hardware emulation since the usable memory size and its bandwidth are limited. The paper proposes the compression method of input sparse matrices for QA emulator. The proposed method uses the sparseness of the coefficient matrix and the reappearance of the same values. An independent table is introduced and data are compressed by the search and registration method of two consecutive data in the value table. The proposed method is applied to Traveling Salesman Problem (TSP) with 32, 64 and 96 cities and Nurse Scheduling Problem (NSP). The proposed method could reduce the amount of data by 1/40 for 96 city TSP and could manage 96 city TSP on the hardware emulator. When applied to NSP, we confirmed the effectiveness of the proposed method by the compression ratio ranging from 1/4 to 1/11.8. The data reduction is also useful for the simulation/emulation performance when using the compressed data directly and 1.9 times faster speed can be found on 96 city TSP than the CSR-based method.
ER -