scholarly journals Quantum computing methods for supervised learning

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Viraj Kulkarni ◽  
Milind Kulkarni ◽  
Aniruddha Pant
2021 ◽  
pp. 143-205
Author(s):  
Jack D. Hidary

2019 ◽  
Vol 117 (15-16) ◽  
pp. 2069-2082 ◽  
Author(s):  
Teng Bian ◽  
Daniel Murphy ◽  
Rongxin Xia ◽  
Ammar Daskin ◽  
Sabre Kais

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Andrew Blance ◽  
Michael Spannowsky

Abstract Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Andriyan Bayu Suksmono ◽  
Yuichiro Minato

AbstractFinding a Hadamard matrix (H-matrix) among all possible binary matrices of corresponding order is a hard problem that can be solved by a quantum computer. Due to the limitation on the number of qubits and connections in current quantum processors, only low order H-matrix search of orders 2 and 4 were implementable by previous method. In this paper, we show that by adopting classical searching techniques of the H-matrices, we can formulate new quantum computing methods for finding higher order ones. We present some results of finding H-matrices of order up to more than one hundred and a prototypical experiment of the classical-quantum resource balancing method that yields a 92-order H-matrix previously found by Jet Propulsion Laboratory researchers in 1961 using a mainframe computer. Since the exactness of the solutions can be verified by an orthogonality test performed in polynomial time; which is untypical for optimization of hard problems, the proposed method can potentially be used for demonstrating practical quantum supremacy in the near future.


2019 ◽  
Author(s):  
Mark Fingerhuth ◽  
Tomáš Babej ◽  
Peter Wittek

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  

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