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

• URL: http://arxiv.org/abs/2506.15760v1
• Date: Wed, 18 Jun 2025 14:56:16 GMT
• Title: Compilation, Optimization, Error Mitigation, and Machine Learning in Quantum Algorithms
• Authors: Shuangbao Paul Wang, Jianzhou Mao, Eric Sakk,
• Abstract summary: This paper discusses the compilation, optimization, and error mitigation of quantum algorithms.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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