量子频率重采样

IF 6.6 1区物理与天体物理 Q1 PHYSICS, APPLIED

npj Quantum Information Pub Date : 2025-07-25 DOI:10.1038/s41534-025-01076-z

Emanuele Tumbiolo, Simone Roncallo, Chiara Macchiavello, Lorenzo Maccone

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引用次数: 0

摘要

在信号处理中，重采样算法可以修改编码数据点集合的资源数量。下采样降低了存储和通信的成本，而上采样从有限的数据中插入新数据，例如，当调整数字图像的大小时。我们提出了一套量子算法工具集，使用量子傅立叶变换来调整高频编码量子位的数量，对量子寄存器概率中编码的数据进行重新采样。我们讨论了优于经典重采样算法的优点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Quantum frequency resampling

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Quantum frequency resampling

In signal processing, resampling algorithms can modify the number of resources encoding a collection of data points. Downsampling reduces the cost of storage and communication, while upsampling interpolates new data from limited one, e.g., when resizing a digital image. We present a toolset of quantum algorithms to resample data encoded in the probabilities of a quantum register, using the quantum Fourier transform to adjust the number of high-frequency encoding qubits. We discuss advantage over classical resampling algorithms.

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来源期刊

npj Quantum Information Computer Science-Computer Science (miscellaneous)

CiteScore

13.70

自引率

3.90%

发文量

130

审稿时长

29 weeks

期刊介绍： The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.