February 15, 2024 | Thomas Lubinski, Joshua J. Goings, Karl Mayer, Sonika Johri, Nithin Reddy, Aman Mehta, Niranjan Bhatia, Sonny Rappaport, Daniel Mills, Charles H. Baldwin, Luning Zhao, Aaron Barbosa, Smarak Maity, Pranav S. Mundada
The paper "Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks" by the Quantum Economic Development Consortium (QED-C) collaboration explores the use of application-oriented benchmarks to gauge the performance of quantum computers in real-world applications. The QED-C suite of benchmarks provides a comprehensive framework to evaluate quantum systems across various dimensions, including result quality, execution time, and resource consumption. The authors address several challenges in broadening the relevance of these benchmarks to more complex applications, such as improving landscape coverage by systematically varying algorithm parameters, adding new benchmarks like a scalable HHL linear equation solver and a Hydrogen Lattice simulation, and analyzing the trade-offs between result quality and run-time cost. They also introduce methods for program optimization and error mitigation, demonstrating their impact on performance. The paper concludes by discussing how the benchmark framework can facilitate the exploration of algorithmic options and their impact on performance, highlighting the importance of these tools in advancing quantum computing technology.The paper "Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks" by the Quantum Economic Development Consortium (QED-C) collaboration explores the use of application-oriented benchmarks to gauge the performance of quantum computers in real-world applications. The QED-C suite of benchmarks provides a comprehensive framework to evaluate quantum systems across various dimensions, including result quality, execution time, and resource consumption. The authors address several challenges in broadening the relevance of these benchmarks to more complex applications, such as improving landscape coverage by systematically varying algorithm parameters, adding new benchmarks like a scalable HHL linear equation solver and a Hydrogen Lattice simulation, and analyzing the trade-offs between result quality and run-time cost. They also introduce methods for program optimization and error mitigation, demonstrating their impact on performance. The paper concludes by discussing how the benchmark framework can facilitate the exploration of algorithmic options and their impact on performance, highlighting the importance of these tools in advancing quantum computing technology.