Quantum Computing for Enhancing AI Models in Healthcare Diagnostics: a Theoretical Perspective
EasyChair Preprint 15355
4 pages•Date: November 1, 2024Abstract
Artificial intelligence (AI) has brought
transformative potential to healthcare, with its uses
extending from diagnostics to personalized care.
However, traditional AI models, including deep
learning networks, face significant challenges in
computational demand, data complexity, and pro-
cessing speed. Quantum computing, with its excep-
tional computational power, offers a promising solu-
tion. This paper examines how quantum computing
can enhance AI models in healthcare diagnostics.
Through analyzing algorithms like Quantum Neural
Networks (QNNs) and Quantum Approximate Opti-
mization Algorithm (QAOA), we provide a theoreti-
cal perspective on the potential for improvements
in diagnostic accuracy, efficiency, and scalability.
The paper highlights the constraints of classical
AI models and how quantum technology could
overcome these limitations, providing new directions
for research into quantum-powered AI in healthcare
Keyphrases: Artificial Intelligence, Healthcare Diagnostics, quantum computing