A model of interacting quantum neurons with a dynamic synapse

Abstract

Motivated by recent advances in neuroscience, in this work, we explore the emergent behaviour of quantum systems with a dynamical biologically-inspired qubits interaction. We use a minimal model of two interacting qubits with an activity-dependent dynamic interplay as in classical dynamic synapses that induces the so-called synaptic depression, that is, synapses that present synaptic fatigue after heavy presynaptic stimulation. Our study shows that in absence of synaptic depression the 2-qubits quantum system shows typical Rabi oscillations whose frequency decreases when synaptic depression is introduced, so one can trap excitations for a large period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be sustained in time by introducing a small energy shift between the qubits. In addition, we report that long time entanglement between the two qubits raises naturally in the presence of synaptic depression. Moreover, we pro- pose and analyse a plausible experimental setup of our 2-qubits system which demonstrates that these results are robust and can be experimentally obtained in a laboratory.

Entanglement detection with classical deep neural networks

“…Second, machine learning techniques can be used to learn quantum operations and to design experiments [19,20]. One specific line of research in these directions are quantum neural networks (QNN), meaning learning models inspired by biological systems [21][22][23].…”