Quantum Machine Learning: Bridging Superposition and Entanglement for Revolution-ary Healthcare Applications
Abstract
Chur Chin
Quantum machine learning (QML) represents a paradigm shift from classical sequential learning by leveraging fundamental quantum mechanical principles of superposition and entanglement. This review examines how quantum computing architectures, particularly through topological quantum chips such as Majorana and superconducting systems like Google’s Willow, enable simultaneous parallel processing across multiple data streams. Using the Multiverse Transformer architecture as a framework, we analyze the mathematical foundations connecting quantum error correction, Riemann zeta function zeros, and holographic principles to practical applications in precision medicine and surgical robotics. The integration of quantum approximate optimization algorithms (QAOA) and variational quantum circuits (VQC) demonstrates potential for achieving near-zero error rates in complex medical procedures through non-local quantum correlations. This work bridges theoretical physics and clinical applications, proposing that quantum coherence mechanisms may fundamentally transform healthcare decision-making systems.
