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.

STUDYING THE EFFECT OF ADAPTIVE MOMENTUM IN IMPROVING THE ACCURACY OF GRADIENT DESCENT BACK PROPAGATION ALGORITHM ON CLASSIFICATION PROBLEMS

2012 ◽  
Vol 09 ◽  
pp. 432-439 ◽  
Author(s):  
MUHAMMAD ZUBAIR REHMAN ◽  
NAZRI MOHD. NAWI
Keyword(s):  
Gradient Descent ◽  
Back Propagation ◽  
Activation Function ◽  
Descent Method ◽  
Learning Rate ◽  
Gradient Descent Method ◽  
Back Propagation Algorithm ◽  
Classification Problems ◽  
Propagation Algorithm ◽  
Adaptive Momentum

Despite being widely used in the practical problems around the world, Gradient Descent Back-propagation algorithm comes with problems like slow convergence and convergence to local minima. Previous researchers have suggested certain modifications to improve the convergence in gradient Descent Back-propagation algorithm such as careful selection of input weights and biases, learning rate, momentum, network topology, activation function and value for 'gain' in the activation function. This research proposed an algorithm for improving the working performance of back-propagation algorithm which is 'Gradient Descent with Adaptive Momentum (GDAM)' by keeping the gain value fixed during all network trials. The performance of GDAM is compared with 'Gradient Descent with fixed Momentum (GDM)' and 'Gradient Descent Method with Adaptive Gain (GDM-AG)'. The learning rate is fixed to 0.4 and maximum epochs are set to 3000 while sigmoid activation function is used for the experimentation. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like Wine Quality, Mushroom and Thyroid disease.

Application of Machine Learning Techniques for Multi Objective Optimization of Response Variables in Wire Cut Electro Discharge Machining Operation

2019 ◽  
Vol 969 ◽  
pp. 800-806
Author(s):  
Sidharth Kumar Shukla ◽  
Amrita Priyadarshini
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Gradient Descent ◽  
Descent Method ◽  
Kerf Width ◽  
Gradient Descent Method ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Off Time ◽  
Time Pulse

Wire Cut Electrical Discharge Machining (WEDM) is a non-conventional thermal machining process which is capable of accurately machine alloys having high hardness or part having complex shapes that are very difficult to be machined by the conventional machining processes. The WEDM finds applications in automobiles, aero–space, medical instruments, tool and die industries, etc. The input parameters considered for WEDM are pulse on time, pulse off time, flushing pressure, servo voltage, wire feed rate and wire tension. Performance of WEDM is mainly assessed by output variables such as, material removal rate (MRR), kerf width (Kw) and surface roughness (Ra) of the work piece being machined. Looking at the need of a suitable optimization model, the present work explores the feasibility of machine learning concepts to predict optimum surface roughness and kerf width simultaneously by making use of experimental data available in the literature for machining of Hastelloy C– 276 using WEDM. In most of the literatures, single objective optimization has been carried out for predicting optimum cutting parameters for WEDM. Hence, the present work presents a methodology that makes use of a machine learning algorithm namely, gradient descent method as an optimization technique to optimize both surface roughness and kerf width simultaneously (multi objective optimization) and compare the results with the existing literatures. It was observed that the input parameters such as pulse on time, pulse off time, and peak current have significant effect on both surface roughness and kerf width. The gradient descent method was successfully used for predicting the optimum values of response variables.

scholarly journals IDENTIFICATION OF AREAS OF CORONAVIRUS COVID-19 INCIDENCE SPREADING BASED ON CLUSTER ANALYSIS METHOD

2021 ◽  
pp. 5-13
Author(s):  
Kseniia Bazilevych ◽  
Ievgen Meniailov ◽  
Dmytro Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Boosting ◽  
Software Product ◽  
Boosting Method

Subject: the use of the mathematical apparatus of neural networks for the scientific substantiation of anti-epidemic measures in order to reduce the incidence of diseases when making effective management decisions. Purpose: to apply cluster analysis, based on a neural network, to solve the problem of identifying areas of incidence. Tasks: to analyze methods of data analysis to solve the clustering problem; to develop a neural network method for clustering the territory of Ukraine according to the nature of the epidemic process COVID-19; on the basis of the developed method, to implement a data analysis software product to identify the areas of incidence of the disease using the example of the coronavirus COVID-19. Methods: models and methods of data analysis, models and methods of systems theory (based on the information approach), machine learning methods, in particular the Adaptive Boosting method (based on the gradient descent method), methods for training neural networks. Results: we used the data of the Center for Public Health of the Ministry of Health of Ukraine distributed over the regions of Ukraine on the incidence of COVID-19, the number of laboratory examined persons, the number of laboratory tests performed by PCR and ELISA methods, the number of laboratory tests of IgA, IgM, IgG; the model used data from March 2020 to December 2020, the modeling did not take into account data from the temporarily occupied territories of Ukraine; for cluster analysis, a neural network of 60 input neurons, 100 hidden neurons with an activation Fermi function and 4 output neurons was built; for the software implementation of the model, the programming language Python was used. Conclusions: analysis of methods for constructing neural networks; analysis of training methods for neural networks, including the use of the gradient descent method for the Adaptive Boosting method; all theoretical information described in this work was used to implement a software product for processing test data for COVID-19 in Ukraine; the division of the regions of Ukraine into zones of infection with the COVID-19 virus was carried out and a map of this division was presented.

scholarly journals An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive

2018 ◽  
Vol 9 (3) ◽  
pp. 1412
Author(s):  
Zribi Ali ◽  
Zaineb Frijet ◽  
Mohamed Chtourou
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Control Method ◽  
Speed Control ◽  
Pso Algorithm ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and preventing the problems of trapping in local minimum. Then, to achieve high-performance speed tracking despite of the existence of varying parameters in the control system, gradient descent method is used to adjust the NNC parameters. The stability of the proposed controller is analyzed and guaranteed from Lyapunov theorem. The robustness and good dynamic performance of the proposed adaptive neural network speed control scheme are verified through computer simulations.

Wireless Brain Wave Classification for Alzheimer’s Patients via Efficient Neural Network Computation

2018 ◽  
Vol 10 (03) ◽  
pp. 1850004
Author(s):  
Grant Sheen
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Descent Method ◽  
Stochastic Gradient Descent ◽  
Normal Person ◽  
Gradient Descent Method ◽  
Step Size ◽  
Brain Wave ◽  
Proposed Model ◽  
Wireless Recording

Wireless recording and real time classification of brain waves are essential steps towards future wearable devices to assist Alzheimer’s patients in conveying their thoughts. This work is concerned with efficient computation of a dimension-reduced neural network (NN) model on Alzheimer’s patient data recorded by a wireless headset. Due to much fewer sensors in wireless recording than the number of electrodes in a traditional wired cap and shorter attention span of an Alzheimer’s patient than a normal person, the data is much more restrictive than is typical in neural robotics and mind-controlled games. To overcome this challenge, an alternating minimization (AM) method is developed for network training. AM minimizes a nonsmooth and nonconvex objective function one variable at a time while fixing the rest. The sub-problem for each variable is piecewise convex with a finite number of minima. The overall iterative AM method is descending and free of step size (learning parameter) in the standard gradient descent method. The proposed model, trained by the AM method, significantly outperforms the standard NN model trained by the stochastic gradient descent method in classifying four daily thoughts, reaching accuracies around 90% for Alzheimer’s patient. Curved decision boundaries of the proposed model with multiple hidden neurons are found analytically to establish the nonlinear nature of the classification.

Data-Driven Approach for Equipment Reliability Prediction Using Neural Network

2011 ◽  
Vol 411 ◽  
pp. 563-566 ◽  
Author(s):  
Feng Ding ◽  
Xing Ben Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Optimization Theory ◽  
Descent Method ◽  
Numerical Control ◽  
Data Driven ◽  
Gradient Descent Method ◽  
Training Algorithm ◽  
Cnc Machine

BP neural network based data-driven method is proposed to predict reliability in this paper. The BP neural network prediction using Gradient Descent Method (GDM), Additional Momentum Gradient Descent Method (AMGDM) and Levenberg-Marquardt Method(L-M) based on numerical optimization theory of training algorithm are compared with different neuron number. The proposed approach is validated via age data collected from computer numerical control (CNC) machine tool in the field. The results from the proposed method show that perfect predicting performance is achieved under considering selecting suitable number of the hidden neurons and training algorithm. Remarks are outlined regarding the fact that BP neural network based on data-driven method is feasible, effective and adequate predicting accuracy can be obtained.

scholarly journals An Improved Gradient Descent Method for Optimization of Supervised Machine Learning Problems

2021 ◽  
Vol 183 (20) ◽  
pp. 39-45
Author(s):  
Dada Ibidapo Dare ◽  
Akinwale Adio Taofiki ◽  
Onashoga Adebukola S. ◽  
Osinuga Idowu A.
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Descent Method ◽  
Supervised Machine Learning ◽  
Learning Problems ◽  
Gradient Descent Method

scholarly journals The Power Cepstrum Calculation with Convolutional Neural Networks

2019 ◽  
Vol 19 (2) ◽  
pp. e13 ◽  
Author(s):  
Mario Alejandro García ◽  
Eduardo Atilio Destéfanis
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Gradient Descent ◽  
Descent Method ◽  
Intermediate Step ◽  
Gradient Descent Method ◽  
The Neural Network ◽  
Regression Problems ◽  
The One ◽  
Deep Learning Model

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.

Sign in / Sign up

Export Citation Format

Share Document