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

TL;DR

This paper explores the compilation, optimization, and error mitigation techniques for quantum algorithms, emphasizing the integration of quantum and classical computing to enhance performance and reliability of quantum computations.

Contribution

It introduces an approximate quantum Fourier transform (AQFT) that improves circuit execution efficiency in hybrid quantum-classical systems, advancing quantum algorithm optimization.

Findings

AQFT enhances quantum circuit efficiency

Hybrid quantum-classical platforms enable exponential speedups

Error mitigation strategies improve quantum algorithm reliability

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.