Quantum Simulation of Synaptic Plasticity Mechanisms in Neural Circuits

Abstract

Synaptic plasticity—the capacity of neural circuits to strengthen, weaken, or reconfigure connections in response to activity—is fundamental to learning, memory formation, and adaptive behavior. Classical computational models have advanced the understanding of plasticity, but they struggle to fully capture the high-dimensional, nonlinear, and quantum-biophysical processes that shape synaptic dynamics at molecular and network scales. This study explores a quantum simulation framework for modeling synaptic plasticity mechanisms by leveraging variational quantum circuits, Hamiltonian-based learning rules, and quantum state representations of neuronal interactions. The proposed approach encodes synaptic weight updates, spike-timing–dependent plasticity (STDP), and Hebbian/anti-Hebbian processes within quantum operators capable of expressing richer correlation structures than classical models. Preliminary results demonstrate that quantum simulations can represent multi-synapse entanglement, non-Markovian memory traces, and complex attractor transitions that approximate biological synaptic behaviors with enhanced precision. These findings highlight the emerging role of quantum computational tools in uncovering new aspects of neural adaptability and advancing next-generation computational neuroscience.