This paper discusses the compilation, optimization, and error mitigation of quantum algorithms, essential steps to execute real-world quantum algorithms. Quantum algorithms running on a hybrid platform with QPU and CPU/GPU take advantage of existing high-performance computing power with quantum-enabled exponential speedups. The proposed approximate quantum Fourier transform (AQFT) for quantum algorithm optimization improves the circuit execution on top of an exponential speed-ups the quantum Fourier transform has provided.
@article{arxiv.2506.15760,
title = {Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms},
author = {Shuangbao Paul Wang and Jianzhou Mao and Eric Sakk},
journal= {arXiv preprint arXiv:2506.15760},
year = {2025}
}