scholarly journals Quantum computing methods for supervised learning

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Viraj Kulkarni ◽  
Milind Kulkarni ◽  
Aniruddha Pant
Keyword(s):  
Quantum Computing ◽  
Supervised Learning ◽  
Computing Methods

Quantum Computing Methods

2021 ◽  
pp. 143-205
Author(s):  
Jack D. Hidary
Keyword(s):  
Quantum Computing ◽  
Computing Methods

scholarly journals Quantum computing methods for electronic states of the water molecule

2019 ◽  
Vol 117 (15-16) ◽  
pp. 2069-2082 ◽  
Author(s):  
Teng Bian ◽  
Daniel Murphy ◽  
Rongxin Xia ◽  
Ammar Daskin ◽  
Sabre Kais
Keyword(s):  
Quantum Computing ◽  
Water Molecule ◽  
Electronic States ◽  
Computing Methods

scholarly journals Quantum machine learning for particle physics using a variational quantum classifier

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Andrew Blance ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Computing ◽  
Particle Physics ◽  
Gradient Descent ◽  
Computing Methods ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Classification Problems ◽  
Quantum Machine Learning

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.

scholarly journals Quantum computing formulation of some classical Hadamard matrix searching methods and its implementation on a quantum computer

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Andriyan Bayu Suksmono ◽  
Yuichiro Minato
Keyword(s):  
Quantum Computing ◽  
Quantum Computer ◽  
Hadamard Matrix ◽  
Computing Methods ◽  
Previous Method ◽  
Mainframe Computer ◽  
Binary Matrices ◽  
Hard Problems ◽  
Classical Quantum ◽  
Near Future

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.

Discrete variable gate-model quantum computing

2019 ◽  
Author(s):  
Mark Fingerhuth ◽  
Tomáš Babej ◽  
Peter Wittek
Keyword(s):  
Quantum Computing ◽  
Discrete Variable

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

An IoT Device Identification Method based on Semi-supervised Learning

2020 ◽  
Author(s):  
Linna Fan ◽  
Shize Zhang ◽  
Yichao Wu ◽  
Zhiliang Wang ◽  
Chenxin Duan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Device Identification ◽  
Identification Method
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