Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance

TL;DR

This paper explores the application of quantum machine learning models, specifically qGAN and QCBM, in finance using simulated environments and real-world datasets, highlighting potential future advantages.

Contribution

It introduces quantum generative models like qGAN and QCBM for financial data, demonstrating their implementation and potential benefits in finance.

Findings

qGAN and QCBM models show promise in financial data generation

Simulated experiments indicate potential quantum advantage in finance

Models outperform classical counterparts in certain scenarios

Abstract

Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we discuss some upcoming active new research areas in application of quantum machine learning (QML) in finance. We discuss certain QML models that has become areas of active interest in the financial world for various applications. We use real world financial dataset and compare models such as qGAN (quantum generative adversarial networks) and QCBM (quantum circuit Born machine) among others, using simulated environments. For the qGAN, we define quantum circuits for discriminators and generators and show promises of future quantum advantage via QML in finance.