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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Summary:<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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