Programming Quantum – Classical Architectures: Preparing for Quantum Computing Research Using GPU and QPU Resources

The rapid advancement of quantum computing necessitates the development of hybrid computing architectures, where Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) collaborate to tackle complex computational problems. As quantum applications continue to emerge, researchers must navigate the intricate landscape of hybrid quantum-classical computing. This tutorial is designed to equip participants with the necessary skills to leverage QPU and GPU resources for conducting quantum computing research.

Through a combination of presentations, hands-on activities, and interactive discussions, attendees will gain practical experience in working with hybrid quantum-classical systems. Together, we will exemplify a potential research workflow using hybrid neural networks as an example covering the fundamentals, working hands-on with the algorithm and executing it on actual quantum computing hardware and iteratively improve it. This will involve applying error mitigation techniques, optimizing quantum circuits, and integrating classical machine learning tools with quantum computing resources. By the end of the tutorial, attendees will have a comprehensive understanding of tools, techniques, and best practices required for conducting research in hybrid quantum-classical computing.

This tutorial also offers a unique opportunity to connect with peers and experts in the field, fostering potential collaborations and knowledge sharing. By the end of the tutorial, attendees will be well-equipped to explore the vast possibilities of hybrid quantum-classical computing and contribute to the advancement of this exciting field.