Alexander McCaskey is a quantum computing software architect at NVIDIA, and the manager of the Quantum Computing Architecture and Algorithms team. His work is focused on programming models, compilers, and languages for heterogeneous quantum-classical computing. He is the lead architect for the CUDA Quantum project, a novel quantum-classical programming model in C++ and Python enabling performant workflows on heterogeneous architectures. Previously, he was a Staff Scientist at Oak Ridge National Laboratory where he led the development of the XACC system-level quantum framework and the QCOR quantum-classical C++ compiler platform. He received B.Sc. degrees in 2010 in Physics and Mathematics from the University of Tennessee, and a M.Sc. degree in physics from the Virginia Polytechnic and State University in 2014.
Presentation Title:
Enabling New Methods for Quantum-Classical Integration at Scale
Presentation Abstract:
2024 marked a turning point for quantum computing: credible demonstrations of quantum error correction (QEC) across multiple qubit modalities and the first glimpses of scalable fault-tolerant architectures. As the field moves beyond noisy-intermediate-scale quantum (NISQ) devices, new algorithmic frameworks and integration strategies are becoming essential for tightly coupling quantum processors with high-performance computing (HPC) and AI. This talk explores how NVIDIA is advancing quantum-accelerated supercomputing through cutting-edge software and algorithmic methods. This includes high-performance, hardware-agnostic simulation tools for algorithm development, compiler frameworks that bridge quantum-classical workflows, and AI-driven approaches to qubit calibration, control, and error correction. Emphasis will be placed on the evolving methods that enable hybrid quantum-classical algorithms, scalable runtime orchestration, and efficient integration of quantum computation into scientific workflows laying the groundwork for the next generation of HPC-ready quantum platforms.