scholarly journals Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 46317-46350 ◽  
Author(s):  
Syed Junaid Nawaz ◽  
Shree Krishna Sharma ◽  
Shurjeel Wyne ◽  
Mohammad N. Patwary ◽  
Md. Asaduzzaman
Keyword(s):  
Machine Learning ◽  
Communication Networks ◽  
State Of The Art ◽  
Quantum Machine Learning ◽  
The Future

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

scholarly journals Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 71
Author(s):  
Frank Phillipson ◽  
Robert S. Wezeman ◽  
Irina Chiscop
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Communication Networks ◽  
Mobile Networks ◽  
Quantum Computers ◽  
Limiting Factor ◽  
Learning Approaches ◽  
Specific Task ◽  
Learning Tasks ◽  
Quantum Machine Learning

Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers may be helpful here, especially where machine learning is one of the areas where quantum computers are expected to bring an advantage. This paper proposes and evaluates three approaches for using quantum machine learning for a specific task in mobile networks: indoor–outdoor detection. Where current quantum computers are still limited in scale, we show the potential the approaches have when larger systems become available.

A Review of Non-Minutiae Based Fingerprint Features

2018 ◽  
Vol 8 (1) ◽  
pp. 32-58 ◽  
Author(s):  
Ravinder Kumar
Keyword(s):  
Machine Learning ◽  
Critical Review ◽  
State Of The Art ◽  
The State ◽  
Fingerprint Matching ◽  
Open Problems ◽  
Future Directions ◽  
Detailed Review ◽  
The Future

This article presents a critical review of extensive research on automatic fingerprint matching over a decade. In particular, the focus is made on the non-minutiae-based features and machine-learning-based fingerprint matching approaches. This article highlights the problems pertaining to the minutiae-based features and presents a detailed review on the state-of-the-art of non-minutiae-based features. This article also presents an overview of the state-of-the-art fingerprint benchmark databases, along with the open problems and the future directions for the fingerprint matching.

scholarly journals Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction

2021 ◽  
Author(s):  
Himanshu Gupta ◽  
Hirdesh Varshney ◽  
Tarun Kumar Sharma ◽  
Nikhil Pachauri ◽  
Om Prakash Verma
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Detection Rate ◽  
Missing Values ◽  
Prediction Models ◽  
State Of The Art ◽  
Outlier Rejection ◽  
Comparative Performance ◽  
Quantum Machine Learning ◽  
Diabetes Prediction

Abstract Background Diabetes, the fastest growing health emergency, has created several life-threatening challenges to public health globally. It is a metabolic disorder and triggers many other chronic diseases such as heart attack, diabetic nephropathy, brain strokes, etc. The prime objective of this work is to develop a prognosis tool based on the PIMA Indian Diabetes dataset that will help medical practitioners in reducing the lethality associated with diabetes. Methods Based on the features present in the dataset, two prediction models have been proposed by employing deep learning (DL) and quantum machine learning (QML) techniques. The accuracy has been used to evaluate the prediction capability of these developed models. The outlier rejection, filling missing values, and normalization have been used to uplift the discriminatory performance of these models. Also, the performance of these models has been compared against state-of-the-art models. Results The performance measures such as precision, accuracy, recall, F1 score, specificity, balanced accuracy, false detection rate, missed detection rate, and diagnostic odds ratio have been achieved as 0.90, 0.95, 0.95, 0.93, 0.95, 0.95, 0.03, 0.02, and 399.00 for DL model respectively, However for QML, these measures have been computed as 0.74, 0.86, 0.85, 0.79, 0.86, 0.86, 0.11, 0.05, and 35.89 respectively. Conclusion The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models. It also uplifts the performance by 1.06% compared to reported work. However, the performance of the QML model has been found as satisfactory and comparable with existing literature.

scholarly journals Nearest centroid classification on a trapped ion quantum computer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sonika Johri ◽  
Shantanu Debnath ◽  
Avinash Mocherla ◽  
Alexandros SINGK ◽  
Anupam Prakash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Computer ◽  
State Of The Art ◽  
Synthetic Data ◽  
Quantum Computers ◽  
Trapped Ion ◽  
Quantum Machine Learning ◽  
Real World Applications ◽  
Promising Area ◽  
Centroid Classifier

AbstractQuantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.

Artificial Intelligence Approaches to Detect Neurodegenerative Disease From Medical Records

2021 ◽  
pp. 254-267
Author(s):  
Abhranil Gupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Medical Records ◽  
State Of The Art ◽  
The State ◽  
Learning Approaches ◽  
Brain Scans ◽  
The Future

This chapter gives a brief overview of the state of the art of machine learning approaches in detection of the neurodegenerative disease from medical records (brain scans, etc.). It starts with an understanding of the sub-field of artificial intelligence, machine learning, then goes to understand neurodegenerative disease, with a focus on four major diseases and then goes on to giving an overview of how such diseases are detected using machine learning. In the end, it discusses the future areas of research that needs to be done in order to improve the field of research.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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