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Quantum Machine Learning
Quantum Discrete Fourier Transform with Classical Output for Signal Processing
arXiv
Authors: Chao-Yang Pang, Ben-Qiong Hu
Year
2007
Paper ID
49946
Status
Preprint
Abstract Read
~2 min
Abstract Words
120
Citations
N/A
Abstract
Discrete Fourier transform (DFT) is the base of modern signal or information processing. 1-Dimensional fast Fourier transform (1D FFT) and 2D FFT have time complexity O(NlogN) and ON2logN respectively. Quantum 1D and 2D DFT algorithms with classical output (1D QDFT and 2D QDFT) are presented in this paper. And quantum algorithm for convolution estimation is also presented in this paper. Compared with FFT, QDFT has two advantages at least. One of advantages is that 1D and 2D QDFT has time complexity O(sqrt(N)) and O(N) respectively. The other advantage is that QDFT can process very long signal sequence at a time. QDFT and quantum convolution demonstrate that quantum signal processing with classical output is possible.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2007 reference point for readers tracking recent quantum research.
- Discrete Fourier transform (DFT) is the base of modern signal or information processing.
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