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Springer eBooks

Quantum-Enhanced Artificial General Intelligence: Bridging Computational Paradigms

Ushaa Eswaran, Vishal Eswaran, Vivek Eswaran, Keerthna Murali

Published Jan 1, 2025• 2 citations
Publisher
DOI
10.1007/978-3-031-87931-9_14

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Quantum computing promises to revolutionize computation by leveraging superposition, entanglement, and quantum interference. This paper examines whether it can bridge the ontological divide between processual reality (Monos) and representational structures (Logos) in the context of Artificial General Intelligence (AGI). Drawing on Metamonism CORE = v1.3, we argue that quantum systems, as currently conceived, accelerate stabilization rather than prevent it, remaining trapped in Logos-dominant paradigms.We distinguish the illusion of an ontological bridge—where quantum processuality serves classical fixation—from a genuine integration that could enable enforced Unfold. While quantum dynamics approximates Monos, measurement enforces Logos, making quantum computing a more efficient optimizer rather than a pathway to non equilibrium cognition.This analysis reframes quantum computing not as an AGI enabler but as a test case for ontological viability in intelligence research and establishes it as a prime example of accelerated fixation, disqualifying it from consideration as a foundation for non-equilibrium cognition.
Abstract: As artificial intelligence (AI) systems grow in complexity, the demand for efficient and scalable computational architectures has become increasingly urgent. This research introduces a hybrid intelligence paradigm that harnesses quantum superalgebraic states as a new computational substrate for accelerating neural network inference and training. By encoding activation patterns and weight transformations into superalgebraic quantum states, we establish a model that combines symbolic expressiveness with quantum parallelism. This framework explores the interaction between quantum algebraic structures—particularly superalgebras like osp(1∣2) and sl(1∣1)—and classical neural network dynamics, enabling enhanced learning efficiency and adaptive generalization. The result is a novel hybrid system that bridges algebra, AI, and quantum computation for next-generation intelligent systems. Keywords: Hybrid Intelligence, Quantum Superalgebras, Quantum Neural Networks, AI Acceleration, Quantum AI, Superalgebraic Encoding, Quantum Parallelism, Algebraic Machine Learning