Toward Quantum General AI: Architecting Quantum Turing Machines for Exponentially Accelerated Reasoning

Abstract

Quantum General Artificial Intelligence (QGAI) represents the theoretical pinnacle of computational science, aiming to achieve human-level intelligence through the harnessing of quantum mechanics. This paper transcends the current focus on Noisy Intermediate-Scale Quantum (NISQ)-era acceleration and addresses the foundational challenge: architecting the information-theoretic engine for true QGAI—the Quantum Turing Machine (QTM) for complex reasoning. We analyze how the quantum properties of superposition and entanglement can be mapped onto the core components of a logical QTM to achieve an exponential acceleration in decision-making, pattern recognition, and inference. The current bottleneck of classical AI, rooted in combinatorial explosion, is reframed as an inherent parallelism problem solvable by a scalable QTM. We propose a three-tiered QTM architecture model (Quantum-Logic-Unit, Entanglement-Engine, and Hybrid-Control) and detail its application for exponentially accelerated risk analysis in Finance and molecular simulation for Personalized Healthcare. Specifically, we project a quadratic speedup (e.g., via Quantum Amplitude Estimation (QAE)) for high-dimensional Monte Carlo simulations in financial stress-testing and an exponential speedup for protein folding optimization in drug discovery, based on current algorithmic advances. This work lays the theoretical and architectural groundwork for the future of truly intelligent computation.