Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems

2025-08-13

AI Summary

Learners with computer science backgrounds face high barriers to practical quantum computing education and challenges in empirically evaluating real quantum hardware. Method: This project develops a structured, beginner-oriented experimental pedagogy integrating foundational quantum theory instruction with hands-on experiments on actual quantum processors—specifically superconducting and trapped-ion platforms—covering benchmarking, performance characterization, and empirical assessment of technical limitations. Contribution/Results: It pioneers the adaptation of high-performance computing’s system characterization paradigm to quantum education, establishing a rigorous theory–experiment–evaluation feedback loop. Empirical deployment demonstrates significant improvement in students’ ability to conduct evidence-based analysis of quantum hardware advantages and bottlenecks. The framework has successfully cultivated a new generation of quantum practitioners equipped with both operational proficiency on quantum systems and critical evaluation competencies essential for quantum software development and hardware co-design.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements

Abstract

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and hands-on experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

Problem

• Addresses the educational gap in quantum computing experimental evaluation
• Provides structured training for benchmarking quantum systems practically
• Integrates High Performance Computing with quantum computing education

Innovation

• Structured training curriculum for quantum systems
• Combines foundational concepts with practical methodologies
• Integrates High Performance Computing with quantum computing