Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms

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{wang2025_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 }
}