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

Electric properties of the water molecule in 1A1, 1B1, and 3B1 electronic states: Refined CASSCF and CASPT2 calculations

1996 ◽  
Vol 94 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Stephane Klein ◽  
Elise Kochanski ◽  
Alain Strich ◽  
Andrzej J. Sadlej
Keyword(s):  
Water Molecule ◽  
Electronic States ◽  
Electric Properties ◽  
Caspt2 Calculations

Quantum Computing Methods

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

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

scholarly journals Intelligent Numerical Computing Methods for Determining the Concentration of Citric Acid by Accurate Measurement and Calculation

2021 ◽  
Vol 2083 (3) ◽  
pp. 032089
Author(s):  
Yueyang Fu
Keyword(s):  
Citric Acid ◽  
Water Molecule ◽  
Acidic Solution ◽  
Computing Methods ◽  
Proton Donor ◽  
Proton Acceptor ◽  
Acid Base ◽  
Hydroxide Ion ◽  
Numerical Computing ◽  
Conjugate Base

Abstract According to the Bronsted-Lowry theory, an acid is a proton donor, and a base is a proton acceptor. An acid-base reaction involves the proton transfer between chemicals, where a base containing hydroxide ion (OH-) accepts a proton (H+) from an acidic solution to form water (Khan,2016). In the above equation, HCl as an acid donates one H+ ion, and NaOH as a base accepts the proton to form one water molecule (H2O). So, a proton from the acid is transferred to the anion of the base. Then, the metal cation (Na+) and the conjugate base anion (Cl-) form the salt NaCl.

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.

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