AAI_2025_Capstone_Chronicles_Combined

Breast Tumor Classification Using Quantum Neural Networks

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demonstrates that quantum neural networks were a possible approach to this problem, however

traditional neural networks could’ve been used as well.

This example, as well as this project, was implemented using Google’s Cirq 7 library in

conjunction with Tensorflow Quantum 8 - which as the name implies is based on the popular

Tensorflow library. Google defines Cirq as “an open source framework for programming

quantum computers” (Google, 2025). Serrano et al. describe the combination of Cirq and

Tensorflow Quantum as “…being more intuitive for advance [software] development” (Serrano,

Pérez-Castillo, & Pianttini, 2022). Quantum deep learning applications are not limited to

quantum neural networks explored in this work: generative adversarial neural networks and

convolutional neural networks are also possible and being researched (Serrano, Pérez-Castillo,

& Pianttini, 2022).

Quantum Neural Network Applications

(Karthinkeyan, Akila, Sumathi, & Poongodi, 2025) cite three applications of quantum

neural networks: pattern recognition, optimization and combinatorial problems, and generative

modeling. This project falls into the bucket of pattern recognition, as features are being utilized

to try to predict if a tumor is cancer or not. A celebrated example for optimization and

combinatorial problems is protein folding (Karthinkeyan, Akila, Sumathi, & Poongodi, 2025).

Failure of protein folding can lead to a collection of diseases and health problems, including

cancer (Zhang, Gong, Wi, & Perrett, 2021). This opens the potential not just to detect cancer, as

in this work, but to prevent it in the first place- and may be the key to unlocking many other

incurable diseases (Kaku, 2023). Finally, generative modeling enables new examples to be

generated based on the characteristics of the training data. This is accomplished by identifying

7 https://quantumai.google/cirq 8 https://www.tensorflow.org/quantum

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