Modeling quantum algorithms for the simulation of biological neural networks

Abstract

This project aims to explore the potential of quantum algorithms in the simulation of biological neural networks. Leveraging quantum cloud computing provided by IBM Quantum, advanced artificial intelligence (AI) techniques are integrated to optimize and improve neural models. Detailed connectivity and gene expression data obtained from the Allen Brain Atlas are used, processed and cleaned to facilitate their use in quantum simulations. The results demonstrate the ability of quantum algorithms to handle the complexity of biological neural networks, providing a powerful tool for research in neuroscience and biology. Quantum computing offers significant advantages in terms of efficiency and scalability for the simulation of complex biological systems. This work details the steps taken, the results obtained and the conclusions derived from the process, highlighting the crucial role of AI in optimizing the parameters of the quantum circuit and in the analysis of the results of the simulations