Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components.

Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components.

<p>Training loss results plotted against the number of iterations for (a) 9, (b) 10, (c) 11, and (d) 12 principal components.</p>

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Bibliographic Details
Main Author:	Aninda Astuti (21736334) (author)
Other Authors:	Pin-Keng Shih (13200121) (author), Shan-Chih Lee (493837) (author), Venugopala Reddy Mekala (21736337) (author), Ezra B. Wijaya (21736340) (author), Ka-Lok Ng (9295900) (author)
Published:	2025
Subjects:	Medicine Cell Biology Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified solving intricate problems improved execution time cancer cell dissemination neural quantum embedding combines quantum computing qnn algorithms compared despite extensive research identifying metastasis biomarkers complex metastasis process qml &# 8217 binary classification problem binary classification hybrid quantum research delves classical algorithms study addresses stage approach offering potential machine learning high lethality drawing inspiration daunting task
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