A Training Procedure for Quantum Random Vector Functional-link Networks

Quantum computing ideally allows designers to build much more efficient computers than the existing classical ones. By exploiting quantum parallelism and entanglement, it is possible to solve signal processing tasks on high throughput data coming from multiple sources. Random Vector Functional-Link is a neural network model usually adopted in such contexts, although quantum implementations have not been considered so far. This paper proposes a quantum version of this neural model, by introducing a specific learning algorithm to find the coefficients of the adopted quantum gates and focusing on the finite precision arithmetic imposed by the qubit strings that are used to represent the model parameters.