Challenges in Hybrid Quantum-Classical Convolutional Neural Networks: Evaluating Qubit Impact on Binary Image Classification
TL;DR
A hybrid quantum-classical convolutional neural network model (HQ-CNN) is presented and its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets is evaluated.
Abstract
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.