Quantum coherence in neuromorphic computing

Abstract

Quantum effects become significant when hardware computing units scale down to nanoscale dimensions. To maintain reliable performance as neuromorphic computing hardware scales down, researchers must understand how quantum coherence across multiple neurons impacts neural network function. In this study, we model neuromorphic computing with quantum coherence effects using a quantum spiking neural network model. We find that quantum coherence between neural activations can alter the network perception, compared to the incoherent network. Destructive interference between activation signals propagating through different synaptic channels drives this effect at the quantum scale. This quantum effect becomes more prominent with increasing network depth and can be mitigated by increasing the number of input neurons connected to each output neuron.