  Integrating Quantum Convolutional Neural Networks (QCNNs) into medical
diagnostics represents a transformative advancement in the classification of
brain tumors. This research details a high-precision design and execution of a
QCNN model specifically tailored to identify and classify brain cancer images.
Our proposed QCNN architecture and algorithm have achieved an exceptional
classification accuracy of 99.67%, demonstrating the model's potential as a
powerful tool for clinical applications. The remarkable performance of our
model underscores its capability to facilitate rapid and reliable brain tumor
diagnoses, potentially streamlining the decision-making process in treatment
planning. These findings strongly support the further investigation and
application of quantum computing and quantum machine learning methodologies in
medical imaging, suggesting a future where quantum-enhanced diagnostics could
significantly elevate the standard of patient care and treatment outcomes.
