This research investigates the practical application of artificial intelligence in digital asset trading to ensure secure transactions on public ledgers. The emphasis lies in predicting prices based on data from digital exchanges, utilizing algorithmic design and seamless automation through APIs across multiple platforms. Additionally, this study explores the integration of quantum computing by visualizing Interference Quantum Processor (IQP) circuits with digital asset prices as annotations. The evaluation methodology incorporates real-time exchange values of Digital Assets, leveraging the collaborative potential of AI, data science, and network capabilities for synchronized valuation. The presented findings underscore the practical application of these technologies in enhancing the efficiency and security of transactions within the dynamic landscape of digital exchanges.