  Existing drug discovery pipelines take 5-10 years and cost billions of
dollars. Computational approaches aim to sample from regions of the whole
molecular and solid-state compounds called chemical space which could be on the
order of 1060 . Deep generative models can model the underlying probability
distribution of both the physical structures and property of drugs and relate
them nonlinearly. By exploiting patterns in massive datasets, these models can
distill salient features that characterize the molecules. Generative
Adversarial Networks (GANs) discover drug candidates by generating molecular
structures that obey chemical and physical properties and show affinity towards
binding with the receptor for a target disease. However, classical GANs cannot
explore certain regions of the chemical space and suffer from
curse-of-dimensionality. A full quantum GAN may require more than 90 qubits
even to generate QM9-like small molecules. We propose a qubit-efficient quantum
GAN with a hybrid generator (QGAN-HG) to learn richer representation of
molecules via searching exponentially large chemical space with few qubits more
efficiently than classical GAN. The QGANHG model is composed of a hybrid
quantum generator that supports various number of qubits and quantum circuit
layers, and, a classical discriminator. QGAN-HG with only 14.93% retained
parameters can learn molecular distribution as efficiently as classical
counterpart. The QGAN-HG variation with patched circuits considerably
accelerates our standard QGANHG training process and avoids potential gradient
vanishing issue of deep neural networks. Code is available on GitHub
https://github.com/jundeli/quantum-gan.
