Quantum speedup dynamics process without non-Markovianity

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
Ying-Jie Zhang ◽  
Xiang Lu ◽  
Hai-Feng Lang ◽  
Zhong-Xiao Man ◽  
Yun-Jie Xia ◽  
...  
Keyword(s):  
Dynamics Process ◽  
Quantum Speedup

scholarly journals Coreset Clustering on Small Quantum Computers

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1690
Author(s):  
Teague Tomesh ◽  
Pranav Gokhale ◽  
Eric R. Anschuetz ◽  
Frederic T. Chong
Keyword(s):  
Quantum Algorithms ◽  
Quantum Computers ◽  
Data Sets ◽  
New Paradigm ◽  
Data Set ◽  
Small Quantum ◽  
Natural Data ◽  
Near Term ◽  
Classical Computing ◽  
Quantum Speedup

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

scholarly journals Quantum enhanced cross-validation for near-optimal neural networks architecture selection

2018 ◽  
Vol 16 (08) ◽  
pp. 1840005 ◽  
Author(s):  
Priscila G. M. dos Santos ◽  
Rodrigo S. Sousa ◽  
Ismael C. S. Araujo ◽  
Adenilton J. da Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Analysis ◽  
Cross Validation ◽  
Quantum Memory ◽  
Classical Algorithm ◽  
Artificial Neural ◽  
Quantum Speedup

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networks (ANN) in superposition. We obtain an exponential quantum speedup in the evaluation of neural networks. We also verify experimentally through a reduced experimental analysis that the proposed algorithm can be used to select near-optimal neural networks.

scholarly journals Quantum Speedup for Active Learning Agents

2014 ◽  
Vol 4 (3) ◽  
Author(s):  
Giuseppe Davide Paparo ◽  
Vedran Dunjko ◽  
Adi Makmal ◽  
Miguel Angel Martin-Delgado ◽  
Hans J. Briegel
Keyword(s):  
Active Learning ◽  
Learning Agents ◽  
Quantum Speedup

scholarly journals Probing for quantum speedup in spin-glass problems with planted solutions

2015 ◽  
Vol 92 (4) ◽  
Author(s):  
Itay Hen ◽  
Joshua Job ◽  
Tameem Albash ◽  
Troels F. Rønnow ◽  
Matthias Troyer ◽  
...  
Keyword(s):  
Spin Glass ◽  
Quantum Speedup

scholarly journals Quantum speedup in the identification of cause–effect relations

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Giulio Chiribella ◽  
Daniel Ebler
Keyword(s):  
Quantum Speedup

scholarly journals Quantum speedup, non-Markovianity and formation of bound state

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bahram Ahansaz ◽  
Abbas Ektesabi
Keyword(s):  
Bound States ◽  
Decisive Role ◽  
Bound State ◽  
Negative Energy ◽  
Quantum Evolution ◽  
Level System ◽  
System Environment ◽  
Speed Up ◽  
The Relationship ◽  
Quantum Speedup

Abstract In this paper, we investigate the relationship between the quantum speedup, non-Markovianity and formation of a system-environment bound state. Previous results show a monotonic relation between these three such that providing bound states with more negative energy can lead to a higher degree of non-Markovianity, and hence to a greater speed of quantum evolution. By studying dynamics of a dissipative two-level system or a V-type three-level system, when similar and additional systems are present, we reveal that the quantum speedup is exclusively related to the formation of the system-environment bound state, while the non-Markovian effect of the system dynamics is neither necessary nor sufficient to speed up the quantum evolution. On the other hand, it is shown that only the formation of the system-environment bound state plays a decisive role in the acceleration of the quantum evolution.

scholarly journals Amplitude estimation without phase estimation

2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Yohichi Suzuki ◽  
Shumpei Uno ◽  
Rudy Raymond ◽  
Tomoki Tanaka ◽  
Tamiya Onodera ◽  
...  
Keyword(s):  
Measurement Data ◽  
Likelihood Estimation ◽  
Estimation Algorithm ◽  
Quantum Circuits ◽  
Phase Estimation ◽  
Amplitude Estimation ◽  
Quantum Amplitude ◽  
Phase Estimation Algorithm ◽  
Near Term ◽  
Quantum Speedup

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.

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