Adaptive Algorithm for Quantum Amplitude Estimation

• URL: http://arxiv.org/abs/2206.08449v1
• Date: Thu, 16 Jun 2022 21:11:15 GMT
• Title: Adaptive Algorithm for Quantum Amplitude Estimation
• Authors: Yunpeng Zhao, Haiyan Wang, Kuai Xu, Yue Wang, Ji Zhu, and Feng Wang
• Abstract summary: We propose an adaptive algorithm for interval estimation of amplitudes. The proposed algorithm uses a similar number of quantum queries to achieve the same level of precision. We rigorously prove that the number of oracle queries achieves $O(1/epsilon)$, i.e., a quadratic speedup over classical Monte Carlo sampling.
• Score: 13.82667502131475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum amplitude estimation is a key sub-routine of a number of quantum algorithms with various applications. We propose an adaptive algorithm for interval estimation of amplitudes. The quantum part of the algorithm is based only on Grover's algorithm. The key ingredient is the introduction of an adjustment factor, which adjusts the amplitude of good states such that the amplitude after the adjustment, and the original amplitude, can be estimated without ambiguity in the subsequent step. We show with numerical studies that the proposed algorithm uses a similar number of quantum queries to achieve the same level of precision $\epsilon$ compared to state-of-the-art algorithms, but the classical part, i.e., the non-quantum part, has substantially lower computational complexity. We rigorously prove that the number of oracle queries achieves $O(1/\epsilon)$, i.e., a quadratic speedup over classical Monte Carlo sampling, and the computational complexity of the classical part achieves $O(\log(1/\epsilon))$, both up to a double-logarithmic factor.

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