Daniel Pompa, a Quantum Solutions Engineer at IonQ, bridges quantum discovery and applications. His background includes physics research in quantum machine learning and prior roles at Raytheon Technologies, Cambridge Quantum Computing, and Quantinuum, where he focused on AI/ML, quantum machine learning, and partnerships. His current interests lie in quantum algorithms, especially applied topology for data analysis and machine learning.
Presentation Title:
Hybrid Computing Approach to Catalyst Design for Drug Synthesis
Presentation Abstract:
IonQ is striving to unlock real-world value with best-in-class partners across the technology ecosystem. In a collaboration with NVIDIA, AstraZeneca, and AWS, the teams focused on building a quantum-accelerated computational chemistry workflow with the power to transform industries like healthcare, life sciences, and materials science. This talk will provide an overview of both the quantum algorithm, Quantum Computing Enhanced Auxiliary Field Quantum Monte Carlo (QC-AFQMC), and the integrated workflow using Forte QPU, NVIDIA CUDA-Q, and Amazon Braket’s ParallelCluster platform, used to model a key step in a nickel-catalyzed reaction. This work is then a preview of how quantum will aid in early-stage discovery, reducing bottlenecks and enabling new capabilities in chemistry and molecular design.