scholarly journals Quantum generative adversarial networks with multiple superconducting qubits

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Kaixuan Huang ◽  
Zheng-An Wang ◽  
Chao Song ◽  
Kai Xu ◽  
Hekang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scale Up ◽  
Generative Adversarial Networks ◽  
Nash Equilibrium Point ◽  
Practical Applications ◽  
Adversarial Networks ◽  
Experimental Implementation ◽  
Challenging Tasks ◽  
Machine Learning Applications ◽  
Near Term

AbstractGenerative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.

scholarly journals A Survey on Generative Adversarial Networks: Variants, Applications, and Training

2022 ◽  
Vol 54 (8) ◽  
pp. 1-49
Author(s):  
Abdul Jabbar ◽  
Xi Li ◽  
Bourahla Omar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Nash Equilibrium ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Crucial Issue ◽  
Practical Applications ◽  
Adversarial Networks ◽  
And Training

The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Machine Learning Data Center Workloads Using Generative Adversarial Networks

2020 ◽  
Vol 48 (2) ◽  
pp. 21-23
Author(s):  
Boudewijn R. Haverkort ◽  
Felix Finkbeiner ◽  
Pieter-Tjerk de Boer
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Learning Data

scholarly journals Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation

2019 ◽  
Vol 214 ◽  
pp. 06025
Author(s):  
Jean-Roch Vlimant ◽  
Felice Pantaleo ◽  
Maurizio Pierini ◽  
Vladimir Loncar ◽  
Sofia Vallecorsa ◽  
...  
Keyword(s):  
Large Scale ◽  
Message Passing Interface ◽  
Scale Up ◽  
High Energy ◽  
Neural Nets ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Data Set ◽  
Distributed Training ◽  
Adversarial Networks

In recent years, several studies have demonstrated the benefit of using deep learning to solve typical tasks related to high energy physics data taking and analysis. In particular, generative adversarial networks are a good candidate to supplement the simulation of the detector response in a collider environment. Training of neural network models has been made tractable with the improvement of optimization methods and the advent of GP-GPU well adapted to tackle the highly-parallelizable task of training neural nets. Despite these advancements, training of large models over large data sets can take days to weeks. Even more so, finding the best model architecture and settings can take many expensive trials. To get the best out of this new technology, it is important to scale up the available network-training resources and, consequently, to provide tools for optimal large-scale distributed training. In this context, our development of a new training workflow, which scales on multi-node/multi-GPU architectures with an eye to deployment on high performance computing machines is described. We describe the integration of hyper parameter optimization with a distributed training framework using Message Passing Interface, for models defined in keras [12] or pytorch [13]. We present results on the speedup of training generative adversarial networks trained on a data set composed of the energy deposition from electron, photons, charged and neutral hadrons in a fine grained digital calorimeter.

scholarly journals Handling Imbalanced Data in Intrusion Detection Systems using Generative Adversarial Networks

2020 ◽  
Vol 2020 (1) ◽  
pp. 1-13
Author(s):  
Ly Vu ◽  
Quang Uy Nguyen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Imbalanced Data ◽  
Original Data ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detection Systems ◽  
Adversarial Networks ◽  
Attack Data

Machine learning-based intrusion detection hasbecome more popular in the research community thanks to itscapability in discovering unknown attacks. To develop a gooddetection model for an intrusion detection system (IDS) usingmachine learning, a great number of attack and normal datasamples are required in the learning process. While normaldata can be relatively easy to collect, attack data is muchrarer and harder to gather. Subsequently, IDS datasets areoften dominated by normal data and machine learning modelstrained on those imbalanced datasets are ineffective in detect-ing attacks. In this paper, we propose a novel solution to thisproblem by using generative adversarial networks to generatesynthesized attack data for IDS. The synthesized attacks aremerged with the original data to form the augmented dataset.Three popular machine learning techniques are trained on theaugmented dataset. The experiments conducted on the threecommon IDS datasets and one our own dataset show thatmachine learning algorithms achieve better performance whentrained on the augmented dataset of the generative adversarialnetworks compared to those trained on the original datasetand other sampling techniques. The visualization techniquewas also used to analyze the properties of the synthesizeddata of the generative adversarial networks and the others.

scholarly journals Counterfactual Examples for Data Augmentation: A Case Study

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Md Golam Moula Mehedi Hasan ◽  
Douglas A. Talbert
Keyword(s):  
Machine Learning ◽  
Potential Application ◽  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Application Area ◽  
Learning Models ◽  
Adversarial Networks ◽  
Feature Values ◽  
Machine Learning Models

Counterfactual explanations are gaining in popularity as a way of explaining machine learning models. Counterfactual examples are generally created to help interpret the decision of a model. In this case, if a model makes a certain decision for an instance, the counterfactual examples of that instance reverse the decision of the model. The counterfactual examples can be created by craftily changing particular feature values of the instance. Though counterfactual examples are generated to explain the decision of machine learning models, in this work, we explore another potential application area of counterfactual examples, whether counterfactual examples are useful for data augmentation. We demonstrate the efficacy of this approach on the widely used “Adult-Income” dataset. We consider several scenarios where we do not have enough data and use counterfactual examples to augment the dataset. We compare our approach with Generative Adversarial Networks approach for dataset augmentation. The experimental results show that our proposed approach can be an effective way to augment a dataset.

scholarly journals Proposed Improvements for Automated Chemical Safety Evaluations Using In-Silico Techniques

2020 ◽  
Author(s):  
Bryan Jordan
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Chemical Space ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Chemical Safety ◽  
Adversarial Networks ◽  
Wide Range ◽  
Traditional Drug

The vastness of chemical-space constrains traditional drug-discovery methods to the organic laws that are guiding the chemistry involved in filtering through candidates. Leveraging computing with machine-learning to intelligently generate compounds that meet a wide range of objectives can bring significant gains in time and effort needed to filter through a broad range of candidates. This paper details how the use of Generative-Adversarial-Networks, novel machine learning techniques to format the training dataset and the use of quantum computing offer new ways to expedite drug-discovery.

scholarly journals Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy

2021 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Akshansh Mishra ◽  
Tarushi Pathak
Keyword(s):  
Machine Learning ◽  
Generative Adversarial Networks ◽  
Microstructure Development ◽  
Machine Learning Technique ◽  
Silicon Alloy ◽  
Aluminum Silicon Alloy ◽  
Adversarial Networks ◽  
Generative Modeling ◽  
Learning Technique ◽  
The Given

Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures.

scholarly journals Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 130 ◽  
Author(s):  
Mohammad Navid Fekri ◽  
Ananda Mohon Ghosh ◽  
Katarina Grolinger
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Consumption ◽  
Smart Grid ◽  
Time Series Data ◽  
Security And Privacy ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.

Usage of generative adversarial networks for edge prediction in ontology graphs

2020 ◽  
pp. 96-103
Author(s):  
О.П. Мосалов
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Computational Experiments ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Machine Learning Model ◽  
Parameter Values

Рассматривается модель машинного обучения для предсказания существования рёбер в графе онтологии, основанная на использовании генеративно-состязательной сети. Проведены вычислительные эксперименты для различных наборов значений гиперпараметров модели. Показано, что модель решает поставленную задачу. Сформулированы направления дальнейшего развития данного подхода. A machine learning model for edge existence prediction in an ontology graph, based on generative adversarial networks, is considered. Computational experiments for different sets of hyper parameter values are fulfilled. It is shown that the model solves the task. Further steps on this approach research are formulated.

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