• Smit Jiwani
• Ruturaj Chondekar
• Azhar Nursumar
• prof. Aishwarya Kamat

Image classification is a fundamental component of computer vision, holding significant implications for a wide array of applications, from medical diagnoses to autonomous driving. Recently, quantum computing has emerged as a promising avenue for enhancing the accuracy and efficiency of image classification. This research investigates the application of Tensorflow Quantum (TFQ) in conjunction with Cirq to harness the potential of quantum computing in binary image classification.The research employs a dual-pronged approach, seamlessly integrating both quantum and classical machine learning methodologies. The central goal is to create a robust binary image classifier that leverages the unique capabilities offered by quantum computing. The research begins by preprocessing image datasets, transforming them into binary classification problems. A critical component of this study is the design of a quantum circuit within the TFQ framework using Cirq. This quantum circuit collaborates with classical neural networks to create a hybrid model, capitalizing on quantum computing's ability to process data concurrently through superposition and to explore intricate feature relationships via entanglement. By infusing quantum principles into the binary image classifier, the model's aptitude for recognizing subtle patterns within images is significantly enhanced, leading to improved classification accuracy.The performance of the quantum binary image classifier is meticulously assessed using established evaluation metrics, including accuracy, precision, recall, and F1 score. Furthermore, the research conducts a comparative analysis against classical machine learning models, such as support vector machines, logistic regression, and convolutional neural networks, to underscore the quantum advantage in image classification. In summary, the fusion of Tensorflow Quantum and Cirq for binary image classification signifies a substantial stride towards harnessing the capabilities of quantum computing for image recognition tasks, with the promise of revolutionizing image classification through superior feature extraction and pattern recognition. This research acts as a catalyst for the exploration of quantum machine learning techniques, showcasing the synergistic potential of combining quantum and classical computing to tackle intricate image classification challenges with precision and efficiency.

Tensorflow Quantum, Cirq, Artifiacial Intelligence, Machine Learning, Binary Image Classification

"BINARY IMAGE CLASSIFICATION WITH TENSORFLOW QUANTUM AND CIRQ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b589-b592, November-2023, Available :http://www.jetir.org/papers/JETIR2311168.pdf

"BINARY IMAGE CLASSIFICATION WITH TENSORFLOW QUANTUM AND CIRQ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb589-b592, November-2023, Available at : http://www.jetir.org/papers/JETIR2311168.pdf