Hybrid Intelligence: Leveraging Superalgebraic Quantum States for Neural Network Acceleration

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