Study on The Effect of Encoding Method in Quantum Machine Learning

被引:0
作者
Xiong, Qingqing [1 ]
Jiang, Jinzhe [2 ,3 ]
Li, Chen [2 ,3 ]
Zhang, Xin [2 ,3 ]
Zhao, Yaqian [2 ,3 ]
机构
[1] Zhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou, Peoples R China
[2] Inspur Elect Informat Ind Co Ltd, Jinan, Peoples R China
[3] Inspur Beijing Elect Informat Ind Co, Beijing, Peoples R China
来源
2024 16TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, ICMLC 2024 | 2024年
关键词
Quantum machine learning; Data encoding; Metrics;
D O I
10.1145/3651671.3651689
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Quantum machine learning algorithms use qubit encoding and quantum circuits to perform feature extraction and pattern recognition. However, the choice of data encoding methods can have a significant impact on the model's performance. To evaluate the effect of different encoding methods in a systematic way, we propose two metrics: distribution distance and distribution radius. These metrics describe how the encoded data distribute in the Hilbert space. We show that there is a positive correlation between prediction accuracy and distribution distance, and a negative correlation between prediction accuracy and distribution radius, both theoretically and experimentally. Based on our findings, we suggest a comparative evaluation of data encoding methods for quantum machine learning, which can help improve the learning efficiency.
引用
收藏
页码:94 / 99
页数:6
相关论文
共 50 条
[31]   Oncological Applications of Quantum Machine Learning [J].
Rahimi, Milad ;
Asadi, Farkhondeh .
TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2023, 22
[32]   Tensor networks for quantum machine learning [J].
Rieser, Hans-Martin ;
Koester, Frank ;
Raulf, Arne Peter .
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2023, 479 (2275)
[33]   Kernel methods in Quantum Machine Learning [J].
Mengoni, Riccardo ;
Di Pierro, Alessandra .
QUANTUM MACHINE INTELLIGENCE, 2019, 1 (3-4) :65-71
[34]   Distributed secure quantum machine learning [J].
Yu-Bo Sheng ;
Lan Zhou .
Science Bulletin, 2017, 62 (14) :1025-1029
[35]   Verifying Fairness in Quantum Machine Learning [J].
Guan, Ji ;
Fang, Wang ;
Ying, Mingsheng .
COMPUTER AIDED VERIFICATION (CAV 2022), PT II, 2022, 13372 :408-429
[36]   Experimental Quantum Embedding for Machine Learning [J].
Gianani, Ilaria ;
Mastroserio, Ivana ;
Buffoni, Lorenzo ;
Bruno, Natalia ;
Donati, Ludovica ;
Cimini, Valeria ;
Barbieri, Marco ;
Cataliotti, Francesco S. ;
Caruso, Filippo .
ADVANCED QUANTUM TECHNOLOGIES, 2022, 5 (08)
[37]   Predicting toxicity by quantum machine learning [J].
Suzuki, Teppei ;
Katouda, Michio .
JOURNAL OF PHYSICS COMMUNICATIONS, 2020, 4 (12)
[38]   Quantum Machine Learning: Perspectives in Cybersecurity [J].
Pastorello, Davide .
COMPUTER SAFETY, RELIABILITY, AND SECURITY. SAFECOMP 2024 WORKSHOPS, 2024, 14989 :266-274
[39]   Distributed secure quantum machine learning [J].
Sheng, Yu-Bo ;
Zhou, Lan .
SCIENCE BULLETIN, 2017, 62 (14) :1025-1029
[40]   Quantum Machine Learning for Malware Classification [J].
Barrue, Gregoire ;
Quertier, Tony .
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT V, 2025, 2137 :245-260