scholarly journals Linear regression by quantum amplitude estimation and its extension to convex optimization

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
Kazuya Kaneko ◽  
Koichi Miyamoto ◽  
Naoyuki Takeda ◽  
Kazuyoshi Yoshino
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Yohichi Suzuki ◽  
Shumpei Uno ◽  
Rudy Raymond ◽  
Tomoki Tanaka ◽  
Tamiya Onodera ◽  
...  

AbstractThis paper focuses on the quantum amplitude estimation algorithm, which is a core subroutine in quantum computation for various applications. The conventional approach for amplitude estimation is to use the phase estimation algorithm, which consists of many controlled amplification operations followed by a quantum Fourier transform. However, the whole procedure is hard to implement with current and near-term quantum computers. In this paper, we propose a quantum amplitude estimation algorithm without the use of expensive controlled operations; the key idea is to utilize the maximum likelihood estimation based on the combined measurement data produced from quantum circuits with different numbers of amplitude amplification operations. Numerical simulations we conducted demonstrate that our algorithm asymptotically achieves nearly the optimal quantum speedup with a reasonable circuit length.


2022 ◽  
Vol 105 (1) ◽  
Author(s):  
Tomoki Tanaka ◽  
Shumpei Uno ◽  
Tamiya Onodera ◽  
Naoki Yamamoto ◽  
Yohichi Suzuki

Author(s):  
Pooja Rao ◽  
Kwangmin Yu ◽  
Hyunkyung Lim ◽  
Dasol Jin ◽  
Deokkyu Choi

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Dmitry Grinko ◽  
Julien Gacon ◽  
Christa Zoufal ◽  
Stefan Woerner

AbstractWe introduce a variant of Quantum Amplitude Estimation (QAE), called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only based on Grover’s Algorithm, which reduces the required number of qubits and gates. We provide a rigorous analysis of IQAE and prove that it achieves a quadratic speedup up to a double-logarithmic factor compared to classical Monte Carlo simulation with provably small constant overhead. Furthermore, we show with an empirical study that our algorithm outperforms other known QAE variants without QPE, some even by orders of magnitude, i.e., our algorithm requires significantly fewer samples to achieve the same estimation accuracy and confidence level.


2020 ◽  
Vol 20 (13&14) ◽  
pp. 1109-1123
Author(s):  
Kouhei Nakaji

In this paper, we introduce an efficient algorithm for the quantum amplitude estimation task which is tailored for near-term quantum computers. The quantum amplitude estimation is an important problem which has various applications in fields such as quantum chemistry, machine learning, and finance. Because the well-known algorithm for the quantum amplitude estimation using the phase estimation does not work in near-term quantum computers, alternative approaches have been proposed in recent literature. Some of them provide a proof of the upper bound which almost achieves the Heisenberg scaling. However, the constant factor is large and thus the bound is loose. Our contribution in this paper is to provide the algorithm such that the upper bound of query complexity almost achieves the Heisenberg scaling and the constant factor is small.


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