Quantum classical hybrid neural networks for continuous variable prediction

TL;DR

This paper introduces a hybrid quantum-classical neural network approach for predicting continuous variables, leveraging quantum machine learning to potentially enhance computational capabilities in finance and other sectors.

Contribution

The paper presents a novel hybrid quantum neural network model specifically designed for continuous variable prediction tasks.

Findings

Demonstrates the feasibility of hybrid quantum neural networks for continuous variable prediction

Shows potential advantages over classical models in certain prediction scenarios

Lays groundwork for future quantum machine learning applications in finance

Abstract

Within this decade, quantum computers are predicted to outperform conventional computers in terms of processing power and have a disruptive effect on a variety of business sectors. It is predicted that the financial sector would be one of the first to benefit from quantum computing both in the short and long terms. In this research work we use Hybrid Quantum Neural networks to present a quantum machine learning approach for Continuous variable prediction.