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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Examinamos a viabilidade do Algoritmo Deutsch-Jozsa, um algoritmo quântico básico, em um problema de regressão logística baseado em aprendizado de máquina. Sua principal propriedade de distinguir o tipo de função com uma aceleração exponencial pode ajudar a identificar a inadequação do recurso muito mais rapidamente. Embora existam condições e restrições estritas, reconfirmamos a superioridade quântica em muitos aspectos da computação moderna.
Jun Suk KIM
Gwangju Institute of Science and Technology
Chang Wook AHN
Gwangju Institute of Science and Technology
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Jun Suk KIM, Chang Wook AHN, "Quantum Algorithm on Logistic Regression Problem" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 856-858, April 2019, doi: 10.1587/transinf.2018EDL8223.
Abstract: We examine the feasibility of Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem. Its major property to distinguish the function type with an exponential speedup can help identify the feature unsuitability much more quickly. Although strict conditions and restrictions to abide exist, we reconfirm the quantum superiority in many aspects of modern computing.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8223/_p
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@ARTICLE{e102-d_4_856,
author={Jun Suk KIM, Chang Wook AHN, },
journal={IEICE TRANSACTIONS on Information},
title={Quantum Algorithm on Logistic Regression Problem},
year={2019},
volume={E102-D},
number={4},
pages={856-858},
abstract={We examine the feasibility of Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem. Its major property to distinguish the function type with an exponential speedup can help identify the feature unsuitability much more quickly. Although strict conditions and restrictions to abide exist, we reconfirm the quantum superiority in many aspects of modern computing.},
keywords={},
doi={10.1587/transinf.2018EDL8223},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - Quantum Algorithm on Logistic Regression Problem
T2 - IEICE TRANSACTIONS on Information
SP - 856
EP - 858
AU - Jun Suk KIM
AU - Chang Wook AHN
PY - 2019
DO - 10.1587/transinf.2018EDL8223
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - We examine the feasibility of Deutsch-Jozsa Algorithm, a basic quantum algorithm, on a machine learning-based logistic regression problem. Its major property to distinguish the function type with an exponential speedup can help identify the feature unsuitability much more quickly. Although strict conditions and restrictions to abide exist, we reconfirm the quantum superiority in many aspects of modern computing.
ER -