NSF Convergence Accelerator - Track C: SQAI: Scalable Quantum Artificial Intelligence for Discovery

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The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This grant will benefit society by compressing the pharmaceutical discovery timeline and reducing cost. This would have societal impacts both economically and in human health. A new branch of artificial intelligence, Generative Adversarial Networks (GAN), shows promise for exploring a large chemical space and generating novel pharmaceutical candidates targeted for a certain disease. Quantum GAN (QGAN) has emerged as a path to accelerate classical GANs. This project will create an experimental quantum GAN framework to explore chemical compounds on Noisy Intermediate Scale Quantum (NISQ) computers. This will yield a new discovery-based framework, Scalable Quantum Artificial Intelligence (SQAI) which could be employed for other applications.This Convergence Accelerator team will answer fundamental questions in the context of drug discovery such as: (i) How to exploit quantum advantage to the fullest for drug discovery using NISQ-era computers? Should we use quantum resources for search, discrimination and reinforcement of reward or allocate them fully for search and rely on classical paradigm for regular tasks? (ii) Does a particular molecular representation benefit NISQ-era computers? (iii) Can we enhance the quantum ansatz in QGAN to explore pharmacologically relevant areas of chemical space? (iv) What is the implication of noise on QGAN during training and generation? (v) Are there other material systems that will provide noise immunity to the existing noise-prone qubits? (vi) What kind of flexibility can we offer to the users in exploring quantum computing for their discovery application? The main tasks are focused on developing a software toolchain to bridge the gap between quantum AI algorithms and hardware, exploring drug discovery using NISQ computers, and preparing a quantum smart and diverse workforce. Outreach to K-12 teachers will include a professional development workshop and curricular materials related to introductory quantum computing content.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.