Dual-frequency quantum phase estimation mitigates the spectral leakage of quantum algorithms
Dual-frequency quantum phase estimation mitigates the spectral leakage of quantum algorithms
Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an integer multiple of the unknown phase, which incurs an accuracy degradation. For the existing single-sample estimation scheme, window-based methods have been proposed for spectral leakage mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramer-Rao bound, when multiple samples are available. Numerical results show that the proposed estimator outperforms the existing window-based methods, when the number of samples is sufficiently high.
Xiong, Yifeng
f93bfe9b-7a6d-47e8-a0a8-7f4f6632ab21
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Long, Gui-Lu
b9a4e55a-8a02-4629-a66d-640aebe2cc78
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
9 June 2022
Xiong, Yifeng
f93bfe9b-7a6d-47e8-a0a8-7f4f6632ab21
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Long, Gui-Lu
b9a4e55a-8a02-4629-a66d-640aebe2cc78
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Xiong, Yifeng, Ng, Soon Xin, Long, Gui-Lu and Hanzo, Lajos
(2022)
Dual-frequency quantum phase estimation mitigates the spectral leakage of quantum algorithms.
IEEE Signal Processing Letters.
Abstract
Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an integer multiple of the unknown phase, which incurs an accuracy degradation. For the existing single-sample estimation scheme, window-based methods have been proposed for spectral leakage mitigation. As a further advance, we propose a dual-frequency estimator, which asymptotically approaches the Cramer-Rao bound, when multiple samples are available. Numerical results show that the proposed estimator outperforms the existing window-based methods, when the number of samples is sufficiently high.
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Accepted/In Press date: 20 April 2022
Published date: 9 June 2022
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Local EPrints ID: 456930
URI: http://eprints.soton.ac.uk/id/eprint/456930
ISSN: 1070-9908
PURE UUID: 31acee67-80ca-4890-b30a-83b7f7c04dfd
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Date deposited: 17 May 2022 16:49
Last modified: 18 Mar 2024 02:48
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