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

Shuangbao Paul Wang, Jianzhou Mao, Eric Sakk
6/18/2025
quant-phcs.LG

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Abstract

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.

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Cite this paper

@article{wang2025compilation,
  title  = {Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms},
  author = {Shuangbao Paul Wang and Jianzhou Mao and Eric Sakk},
  year   = {2025},
  eprint = {2506.15760},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.15760v1}
}

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