Quantum random walk is the quantum counterpart of a classical random walk. The classical
random walk concept has long been used as a computational framework for designing …

The last few decades have seen significant breakthroughs in the fields of deep learning and
quantum computing. Research at the junction of the two fields has garnered an increasing …

Electromagnetic induction current is generated between the adjacent neurons in neural
network caused by the existence of membrane potential difference. Memristor is the fourth …

Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to
their ability to improve performance. However, verifying the success of open QW is …

This paper presents a third-order non-ideal memristor synapse-coupled bi-neuron Hopfield
neural network (HNN), in which a non-ideal memristor synapse is employed to generate an …

This paper studies a cascade tri-neuron Hopfield neural network (CTN-HNN) with no
connection between the first neuron and the third neuron. Such incompletely connected …

To demonstrate and elucidate bursting patterns and their bifurcation mechanisms, a two-
neuron Hopfield neural network is proposed in this paper. The proposed non-autonomous …

Vector vortex beams simultaneously carrying spin and orbital angular momentum of light
promise additional degrees of freedom for modern optics and emerging resources for both …

Physical neural networks (PNN) using physical materials and devices to mimic synapses
and neurons offer an energy-efficient way to implement artificial neural networks. Yet …

As random operations for quantum systems are intensively used in various quantum
information tasks, a trustworthy measure of the randomness in quantum operations is highly …