Cross-Platform Testing of Quantum Computing Platforms

Quantum computing has been attracting the attention of both applied research and companies. Continuous progress on fundamental hardware technology promises to bring us more reliable and large-scale quantum computers on which to run the next generation of quantum algorithms. These programs are compiled and executed on dedicated platforms, and similarly to classical programs, a large effort is required to test these platforms and create a robust software toolchain. Unlike previous studies which focused on cross-optimization and cross-backend testing, this dissertation aims to create the first approach for cross-platform testing which compares execution on diverse quantum computing platforms. To inform the design of the method, we will first perform an empirical study of bugs in quantum computing platforms and a review of the characteristics of realistic quantum programs. The final approach for cross-platform testing will include three components: a learning-based method to generate realistic quantum programs, an approach to map and run them on multiple platforms, and finally a quantum-specific statistical test to compare two multivariate binary distributions returned as the output of quantum programs.