December 2025 OLCF User Conference Call: Quantum Monte Carlo on a Trapped-Ion Quantum Computer
Quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) with quantum tomography matchgate shadows is a promising near-term quantum algorithm with the potential to deliver practical value in quantum chemistry and materials science. However, prior studies have raised significant concerns about its viability, citing the prohibitive cost of classical post-processing. In this work, we demonstrate that, through algorithmic innovations and highly optimized implementations leveraging state-of-the-art NVIDIA GPUs, we achieve several orders of magnitude speedup in the post-processing step compared to previous approaches. We further showcase an end-to-end workflow modeling a transition metal catalyzed synthetic reaction, executed on IonQ’s Forte quantum computer. This 24-qubit simulation represents the largest QC-AFQMC matchgate shadow experiment performed on quantum hardware to date. Combined with advanced error mitigation techniques, our results achieve accuracy competitive with leading classical methods. These advances mark a substantial step toward the practical application of quantum computing in real-world quantum chemistry simulations.
