qLEET: Visualizing Loss Landscapes, Expressibility, Entangling Power and Training Trajectories for Parameterized Quantum Circuits

We present qLEET, an open-source Python package for studying parameterized quantum circuits (PQCs), which are widely used in various variational quantum algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables the computation of properties such as expressibility and entangling power of a PQC by studying its entanglement spectrum and the distribution of parameterized states produced by it. Furthermore, it allows users to visualize the training trajectories of PQCs along with high-dimensional loss landscapes generated by them for different objective functions. It supports quantum circuits and noise models built using popular quantum computing libraries such as Qiskit, Cirq, and Pyquil. In our work, we demonstrate how qLEET provides opportunities to design and improve hybrid quantum-classical algorithms by utilizing intuitive insights from the ansatz capability and structure of the loss landscape.Comment: 11 pages, 8 figures (Main Text) and 8 pages, 6 figures (Supplementary

Artificial Intelligence in Cybersecurity: A New Paradigm Revolutionizing Threat Intelligence and Defense Mechanism

The frequency and proficiency of Cyber-attacks have been increasing lately, this transition necessitates a more robust and protected security defense practices such as near-immediate detection, analysis, and defense by cyber security and forensic specialists as the conventional defense mechanisms founded on the principles of empirical and pattern-based methods is failing to match the pace. With the constant rise in technology and inventions, data consumption and is expected to touch the mark of 181 zettabytes by the end of 2025. Cybersecurity is a sincere concern to a lot of organizations because most of them are using Internet-connected data devices paving the way for cyber attackers. Cyber threat intelligence (CTI) analyzes the data to show the patterns of potential cyber-attacks and forecast the behaviors of bad actors. Based on the depth of intelligence and targeted audience, there are three major CTI types; strategic, tactical, and operational. While CTI practices are not completely eminent it is an iterative process; therefore, it lets organizations enhance their defense approach against emerging cyber threats. Not only the frequency but also the complexity of attacks has increased over the years resulting in successful intrusions with more severe forms of security breaches, this calls for outstanding threat intelligence within the cyber sphere requiring the knowledge base of threat information and a meaningful approach to express this language. In the past, CTI has been treated as a reactive defense measure used after the fact, security teams would collect and store threat intelligence to analyze an attack that had already happened, hoping to glean insights for future similar attack scenarios but in recent times the cybersecurity approach has changed from reactive to proactive. However, as technology advances, defenders can now unlock the power of automation and AI, enabling companies to move into a new era of proactive threat intelligence in which cyber defenders can take advantage of security signals in near real-time. The integration of Artificial Intelligence (AI) with Cyber Threat Intelligence (CTI) marks a transformative era in cybersecurity, addressing the increasing sophistication and frequency of cyber-attacks. The traditional security defenses were mostly reliant on empirical and pattern-based methods, and they are slowly becoming inadequate against the dynamic nature of cyber threats. Studying the AI\u27s capability to automate and enhance the CTI cycle from requirement gathering to feedback presents a modern proactive approach to cybersecurity. Organizations can significantly reduce the detection time of cyber intrusions, automate threat responses, and refine their defense mechanisms against emerging threats by leveraging AI which shows promising impact towards proactive defense practices. Through AI Cybercrooks will benefit from widespread deployment of advanced AI tools before their targets can set up AI in their own defense. By meticulously using innovations such as machine learning, deep learning, and natural language processing we can enable the identification of malicious patterns and unusual activities with unprecedented speed and accuracy. However, this technological advancement also necessitates a secure implementation of AI to thwart adversaries from exploiting AI-powered systems

Prediction for the 2020 United States Presidential Election using Linear Regression Model

The paper identifies various crucial factors, economic and non-economic, essential for predicting the 2020 United States presidential election results. Although it has been suggested by the contemporary discussions on the subject of United States presidential election that inflation rate, unemployment rate, and other such economic factors will play an important role in determining who will win the forthcoming United States Presidential Elections in November, it has been found in this study that, non-economic variables have a significant influence on the voting behaviour. Various non-economic factors like the performance of the contesting political parties in the midterm elections, the June Gallup Rating for the incumbent President, Average Gallup rating during the tenure of the incumbent President, Gallup Index, and Scandals of the Incumbent President were found to have a massive impact on the election outcomes. In the research conducted by Lewis-Beck and Rice (1982) , it was proposed that the Gallup rating for the Incumbent President, obtained in the month of June of the election year, is a significant factor in determining the results of the Presidential Elections. The major reason behind obtaining the Gallup Rating in June of the election year, post-primaries and pre-conventions, is that it is a relative political calm period. However, it has been found in this study that despite the existence of a relationship between the vote share of the incumbent President and his Gallup rating for June, the said Gallup rating cannot be used as the only factor for forecasting the results of the Presidential Election. The influence of all the aforementioned economic and non-economic factors and some other factors on the voter's voting behavior in the forthcoming United States Presidential Election is analyzed in this paper. The proposed regression model in the paper forecasts that Republican party candidate Donald Trump would receive a vote share of 46.74 ± 2.638%

Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression

This paper aims at determining the various economic and non-economic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other non-economic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and non-economic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election

Classification of Atrial Arrhythmias using Neural Networks

Electrocardiogram (ECG) is an important tool used by clinicians for successful diagnosis and detection of Arrhythmias, like Atrial Fibrillation (AF) and Atrial Flutter (AFL). In this manuscript, an efficient technique of classifying atrial arrhythmias from Normal Sinus Rhythm (NSR) has been presented. Autoregressive Modelling has been used to capture the features of the ECG signal, which are then fed as inputs to the neural network for classification. The standard database available at Physionet Bank repository has been used for training, validation and testing of the model. Exhaustive experimental study has been carried out by extracting ECG samples of duration of 5 seconds, 10 seconds and 20 seconds. It provides an accuracy of 99% and 94.3% on training and test set respectively for 5 sec recordings. In 10 sec and 20 sec samples it shows 100% accuracy. Thus, the proposed method can be used to detect the arrhythmias in a small duration recordings with a fairly high accuracy