Quantum Computing Application benchmaRK

Quantum computing application benchmarking aims to rigorously assess the performance of quantum hardware and algorithms across diverse tasks. Current research focuses on developing standardized benchmarks for combinatorial optimization problems and quantum machine learning, employing techniques like quantum annealing, transformer-based circuit generation (e.g., KetGPT), and Bayesian inference for noise mitigation. These efforts are crucial for advancing quantum technology by enabling fair comparisons of different quantum systems and algorithms, ultimately accelerating the development of practical quantum applications.