QUANTUM NEURAL NETWORKS
Image classification plays a crucial role in many machine-learning applications. Numerous classification techniques based on quantum machine learning have been reported recently. In this use case, we deploy the Quanvolutional Neural Network—a hybrid quantum-classical image classification technique inspired by the convolutional neural network, which has the potential to outperform current image processing techniques.
Users can load the train/val/test folders and train/test the model similarly to classical machine learning models. This hybrid approach leverages quantum computing to enhance feature extraction and classification accuracy, paving the way for advanced image processing capabilities.
KEY FEATURES
1. Quantum Circuit Integration: Combines classical neural networks with quantum circuits for enhanced computational power.
2. Quantum Superposition and Entanglement: Leverages quantum principles to explore multiple states simultaneously, leading to potentially faster learning processes.
3. Gradient Descent Optimization: Adapts gradient-based optimization techniques to the quantum domain for training quantum neural networks.
4. Quantum Feature Mapping: Maps classical data into a quantum Hilbert space, enabling more complex data representations.
5. Hybrid Architectures: Integrates classical and quantum layers to maximize the strengths of both computational paradigms.