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