Exploring Potential of Quantum Computing in Classification of Leukemic Cell

Quantum Computing have emerged as helpful tools for a variety of fields, including chemistry, cultivation, processing natural languages, and health care thanks to rapidly increasing computer power and developments in machine learning approaches. In terms of health care applications, quantum computing has recently grown to be a very difficult field. Consequently, quantum machine learning emerged as a popular and useful method for categorization and data processing in a variety of fields. This work uses quantum computing to identify leukemic cells in blood, a field in the health care industry. The suggested methodology uses a Quantum Support Vector Classifier (QSVC) to assist obtain an accuracy of 81.8% for correctly predicted labels relative to the total number of labels.

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Published
2024-09-04
DOI
https://doi.org/10.1109/globalaisummit62156.2024.10947977
Primary Topic
Molecular Communication and Nanonetworks
Type
article
Field-Weighted Citation Impact
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article

Exploring Potential of Quantum Computing in Classification of Leukemic Cell

Ankit Bhatt, Shuchi Bhadula, Sachin Sharma
Molecular Communication and Nanonetworks
article

Exploring Potential of Quantum Computing in Classification of Leukemic Cell

Ankit Bhatt, Shuchi Bhadula, Sachin Sharma
article en

Abstract

Quantum Computing have emerged as helpful tools for a variety of fields, including chemistry, cultivation, processing natural languages, and health care thanks to rapidly increasing computer power and developments in machine learning approaches. In terms of health care applications, quantum computing has recently grown to be a very difficult field. Consequently, quantum machine learning emerged as a popular and useful method for categorization and data processing in a variety of fields. This work uses quantum computing to identify leukemic cells in blood, a field in the health care industry. The suggested methodology uses a Quantum Support Vector Classifier (QSVC) to assist obtain an accuracy of 81.8% for correctly predicted labels relative to the total number of labels.

Graphic Era University (IN)
Openalex Percentile: Top 75%
Molecular Communication and Nanonetworks
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