Quantum Memory Units for Compact Learning-Memory Separation in Neural Networks

Memory is a fundamental component of intelligent systems, yet most neural architectures implement memory through dense hidden states, attention caches, or state-space transitions that entangle feature learning and memory storage within the same computational substrate. Inspired by the functional separation between learning and memory in biological systems, we propose Quantum Memory Units (QMUs), a scalable quantum-inspired memory module for hybrid classical-quantum neural networks. In this framework, classical neural networks extract task-relevant features, while parameterized quantum circuits encode compact memory states through interactions between input qubits and memory qubits. We instantiate two forms of QMU: an episodic QMU for retaining temporal histories through entanglement-mediated sequence compression, and a semantic QMU for aggregating feature-level statistical regularities. To couple classical observations with quantum-inspired memory states, we further introduce a Kalman-filter-based fusion mechanism. We evaluate QMU-enhanced networks on five memory-related tasks, including function approximation, Mackey-Glass prediction, permuted sequential MNIST classification, character-trajectory classification, and copy-memory. The results show that QMUs achieve strong performance on selected episodic memory tasks and competitive early-stage learning behavior under compact parameterization. Additional ROC-based evaluations further indicate that QMU representations can provide meaningful discriminative ability on classification tasks.