Binary Image Classification on Fashion-MNIST Using TensorFlow-Quantum and CIRQ
Keywords:
TensorFlow-Quantum, CNN, QNN, CirqAbstract
TensorFlow and Cirq, two key Google frameworks, are used to process the binary image classification on the dataset. These frameworks were developed by Google. The binary image classification is utilized most frequently in the process of distinguishing an object from its background. The process of segmentation makes it possible to name each pixel as either background or object and then assign black and white colors that match to those labels. The combination of machine learning with quantum computing will lead to a classification that is superior to that achieved by machine learning classification techniques. The TensorFlow Quantum (TFQ) library is a quantum machine learning framework that enables quick prototyping of hybrid quantum-classical ML models. This method proposes using the TFQ library. In order to process the categorization, QNN and CNN are both used as algorithms. Existing challenges for binary image classification include overfitting, a limited amount of data, variability in picture data, and background noise. These challenges are all interrelated. The quantum machine learning methodology that has been developed has the potential to reduce problems such as variability in image data, optimize the background noise that has been discovered in the images, and minimize the overfitting that occurs in the image data.
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