AAI_2025_Capstone_Chronicles_Combined

Breast Tumor Classification Using Quantum Neural Networks

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Abstract

Breast cancer claims the lives of tens of thousands of Americans each year. The survival rate is

90% over 5 years, but that also requires that breast cancer be detected to be treated. Detecting

this cancer is a binary classification problem: the tumor is either cancer or not. Classification

problems are a well explored space in artificial intelligence. Quantum computation allows for

certain problems to be solved that could never be done on existing (classical) computers by

performing on quantum bits (qubits) instead of normal bits. Unlike a bit, these qubits can take on

an infinite number of states between 0 and 1. This work explores using quantum computing with

AI to detect breast cancer, achieving over 90% accuracy utilizing just 10 qubits in a quantum

neural network. While the performance isn’t necessarily impressive, doing the same with 10

(classical) bits is a tall order. The hope is that this serves as a demonstration of the potential for

quantum computing and artificial intelligence to solve real world problems.

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