Quantum Convergence and Divergence Neural Networks

Abstract:

This report presents several novel approach's to neural network design, including the incorporation of a recursive learning rate adjustment mechanism that balances convergence and divergence. The proposed Quantum Convergence and Divergence Neural Network (QCDNN) utilizes established concepts in machine learning, including mean squared error loss, stochastic gradient descent, and recursive learning rate adjustment. The network architecture consists of two fully connected layers, with a simple yet effective design. The recursive learning rate adjustment mechanism checks for convergence or divergence after each epoch, adjusting the learning rate to ensure a balance between the two. This approach enables the network to adapt to changing conditions, preventing stagnation and promoting efficient training. The QCDNN is evaluated on a sample dataset, demonstrating its ability to converge and diverge in response to changing conditions. The report concludes with suggestions for future improvements, including the use of more sophisticated optimizers and the exploration of alternative loss functions. Overall, the QCDNN offers a promising approach to neural network design, with potential applications in a range of fields, from artificial intelligence to physics and economics.

Secondarily the Stone Indexed Cube Neural Network is a multi input Predictive Neural Network.

Tirtiarily the Stone chain network is available.

Quaternarilly the Neural State Prediction Neural Network Chain