One of the most promising approaches to using quantum computing to tackle challenging artificial intelligence issues is quantum machine learning (QML). The majority of QML architectures, however, are constrained by the absence of explicit methods for managing temporal and memory dependencies, which are essential for tasks like signal processing, sequential decision-making and forecasting. By embedding memory through devices like memristors, neuromorphic computing which draws inspiration from the brain's synaptic plasticity-offers a natural solution. In this paper, we propose a conceptual framework for using quantum memristors to incorporate neuromorphic memory into quantum machine learning. We compare the potential benefits over current QML models, suggest a simulation-based experimental design, and assets the extent to which systems could handle sequential data challenges. Our approach contributes towards shaping the emerging paradigm of neuromorphic quantum intelligence.
Kumar et al. (Thu,) studied this question.