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

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

Paul Wang, Jianzhou Mao and Eric Sakk, Morgan State University, USA

Abstract

This paper discusses the compilation, optimization, and error mitigation of quantum algo-rithms, 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.

Keywords

Transpilation, Optimization, Error Mitigation, quantum machine learning, quantum algo-rithms

Full Text : https://aircconline.com/csit/papers/vol15/csit150501.pdf
Abstract URL: https://aircconline.com/csit/abstract/v15n05/csit150501.html
Volume URL : https://airccse.org/csit/V15N05.html

#machinelearning #optimization #quantumalgorithms #compilation

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