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.
In this paper, a new algorithm for on-line learning of Random Vector Functional-Link neural network is proposed. It is specifically tailored to hardware implementations with finite precision arithmetic in distributed computing scenarios, where the massive use of low-cost hardware resources for sensor networks or computing agents is necessary in order to deal with big data, IoT paradigms and multiple sources of information. The proposed algorithm does not require any specific DSP operation to be implemented in hardware, like matrix inversions or multiplications, for real-time learning.
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma
