Quantum Honest Byzantine Agreement as a Distributed Quantum Algorithm

TL;DR

This paper introduces a hybrid quantum/classical neural network inspired by quantum honest Byzantine agreement, using a feedback mechanism and quantum neurons to achieve consensus through coincidence detection.

Contribution

It presents a novel approach combining quantum agreement protocols with neural network concepts, introducing a feedback mechanism and quantum neurons for hybrid learning.

Findings

Proposes a new quantum agreement protocol based on coincidence.

Develops a hybrid quantum/classical neural network model.

Demonstrates potential for quantum-inspired machine learning architectures.

Abstract

We suggest that the Quantum Honest Byzantine Agreement (QHBA) protocol [1] essentially reduces consensus to coincidence. The volume of coincidence is the parameter that drives a receiver to echo its input. A lack of coincidence results in no output from a receiver. This is a similar mechanism therefore to the learning mechanism in cognitive modular neural architectures like Haikonen's architecture [2]. We introduce a simple feedback mechanism and quantum neuron to realize a hybrid quantum / classical machine learning network of simple nodes.