Junde Li

Small Molecule Drug Discovery Using Quantum Machine Learning

Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches such as, Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards the target receptor. However, classical GANs are inefficient 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.

3 Key Takeaways: 1) QGAN-HG requires only a fraction of parameters to learn molecular distribution as efficiently as classical counterpart.

2) QGAN-HG with patched circuits accelerates standard QGAN-HG training process

3) QGAN-HG with patched circuits avoids potential gradient vanishing issue of deep neural networks.

Junde Li is a doctoral student in the Department of Computer Science and Engineering at The Pennsylvania State University since 2019. His research interests include quantum computing, machine learning, and hybrid quantum classical machine learning and optimization for drug discovery and robust perception for autonomous vehicles.

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