Quantum Neural Oscillators with Temporal Memory: A Comprehensive Analysis of Brain-Inspired Quantum Computing

Michael Warrington

Michael Warrington

Eva Andrés, Manuel Pegalájar Cuéllar, Gabriel Navarro

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

Michael Warrington

Ada Öztürkmen

Psychological disorders are increasingly conceptualized as disruptions in large-scale brain network dynamics rather than impairments confined to isolated neural regions. Although classical computational and statistical approaches have advanced the study of neural connectivity, they often fall short in representing the nonlinear, probabilistic, and context-dependent characteristics of brain activity. Quantum simulation offers an alternative modeling framework by employing quantum-inspired mathematical principles—including superposition, entanglement, and interference—to describe complex neural interactions and their temporal evolution. This paper investigates the potential of quantum-inspired simulation techniques to model brain network dynamics associated with psychological disorders such as depression, anxiety, schizophrenia, and neurodevelopmental conditions. By representing neural systems as probabilistic networks capable of occupying multiple functional states simultaneously, quantum-based models provide a powerful means of capturing cognitive variability, network instability, and pathological state transitions. Drawing on insights from neuroscience, quantum cognition, and computational psychiatry, the study highlights how quantum-inspired simulations may contribute to improved mechanistic understanding, predictive modeling, and personalized approaches to mental health treatment.

Eva Andrés, Manuel Pegalájar Cuéllar, Gabriel Navarro

Recent advances in the fields of deep learning and quantum computing have paved the way for innovative developments in artificial intelligence. In this manuscript, we leverage these cutting-edge technologies to introduce a novel model that emulates the intricate functioning of the human brain, designed specifically for the detection of anomalies such as fraud in credit card transactions. Leveraging the synergies of Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, our approach is developed in two distinct stages, closely mirroring the information processing mechanisms found in the brain's sensory and memory systems. In the initial stage, similar to the brain's hypothalamus, we extract low-level information from the data, emulating sensory data processing patterns. In the subsequent stage, resembling the hippocampus, we process this information at a higher level, capturing and memorizing correlated patterns. We will compare this model with other quantum models such as Quantum Neural Networks among others and their corresponding classical models.