Quantum Computing Model for Human Semantic Memory

Understanding the structures of human semantic memory search has been a critical initiative in cognitive science.One testing method is the semantic memory task, which asks patients to recall as many words as possible within a specific category, often to test for neurological disorders.Recent studies have shown that classical computers can simulate brain mechanisms using random behaviors.But unlike classical models, which assume that semantic activation spreads along a single probabilistic pathway at a time, quantum random walks allow for simultaneous exploration of multiple conceptual associations, reflecting the parallel and context-dependent retrieval mechanisms observed in cognition through the use of superposition and interference.To address this, we introduce a quantum random walk computation approach for more detailed modeling to address this.By developing the first quantum computing procedure for classical INVITE random walks using quantum random walks, we provide the maximum probabilities of generating semantically related word categories pertinent to the semantic fluency task in assessing a patient's potential semantic memory recall for cognitive diseases.Thus, this method promises the potential for various quantum search algorithms and offers greater scalability in statistical analysis.