Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance
This work addresses financial modeling challenges for researchers and practitioners, but it is incremental as it builds on existing quantum machine learning methods without major breakthroughs.
The paper explores quantum machine learning models like qGAN and QCBM in finance using real-world datasets, showing potential for future quantum advantage in financial applications.
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.