  In this article, we present a hybrid quantum-classical generative adversarial
network (GAN) for near-term quantum processors. The hybrid GAN comprises a
generator and a discriminator quantum neural network (QNN). The generator
network is realized using an angle encoding quantum circuit and a variational
quantum ansatz. The discriminator network is realized using multi-stage
trainable encoding quantum circuits. A modular design approach is proposed for
the QNNs which enables control on their depth to compromise between accuracy
and circuit complexity. Gradient of the loss functions for the generator and
discriminator networks are derived using the same quantum circuits used for
their implementation. This prevents the need for extra quantum circuits or
auxiliary qubits. The quantum simulations are performed using the IBM Qiskit
open-source software development kit (SDK), while the training of the hybrid
quantum-classical GAN is conducted using the mini-batch stochastic gradient
descent (SGD) optimization on a classic computer. The hybrid quantum-classical
GAN is implemented using a two-qubit system with different discriminator
network structures. The hybrid GAN realized using a five-stage discriminator
network, comprises 63 quantum gates and 31 trainable parameters, and achieves
the Kullback-Leibler (KL) and the Jensen-Shannon (JS) divergence scores of 0.39
and 0.52, respectively, for similarity between the real and generated data
distributions.
