Integer Factorization

Integer factorization, the problem of finding the prime factors of a composite number, remains a significant challenge in computer science, with implications for cryptography. Current research explores novel approaches using neural networks, including deep learning models and vector symbolic architectures, to improve factorization speed and efficiency. These methods often leverage techniques like denoising diffusion processes or adaptations of classical algorithms (e.g., Fermat's method) to formulate the problem in a way amenable to machine learning solutions. Success in this area could impact the security of widely used cryptographic systems.