  Some of the most impressive achievements of contemporary Machine Learning
systems comes from the GAN (Generative Adversarial Network) structure. DALLE-2
and GPT- 3, two of the most impressive and recognizable feats of ML in recent
years, were both trained using adversarial techniques. The world of Quantum
Computing is already well aware of the value of such techniques on near-term
Quantum Hardware: QGANs provide a highly efficient method for loading classical
data into a quantum state. We investigate the performance of these techniques
in an attempt to determine some of the optimal training parameters in a
Qiskit-style Parameterized Circuit QGAN framework.
