Quantum Computing is a field that is gaining recognition at a rapid rate around the world. This review shall address the unique elements of quantum computing with respect to classical computing, the current situation of quantum computing (i.e. Noisy Intermediate Scale Quantum or NISQ era computers) and the potential of such technology for the future. The review further seeks to discuss the problems that need to be overcome to attain quantum computing feasibility and the use of hybrid computing as an alternative that can derive from the benefits of the quantum world.

With the passage of time, the ability of intelligent software in making predictions of seemingly random, chaotic events increases. Most real-world uses of multi-layer perceptrons are in highly chaotic environments. Such environments include the prediction of economic trends and weather phenomena to the modelling of human behaviour. This study aims to analyse the power of Multi-Layer Perceptrons (MLPs) in predicting the outcome of sporting events, in particular, a football (soccer) game. Any sporting event is dominated by chaos, where small changes in the environment can drastically affect the outcome. This study does the same by predicting, based on multi-dimensional data, the point return of English Premier League (EPL) players in the official Fantasy Premier League forum (FPL). The study uses MLPs as a means to optimise FPL team selection and thus predicts an average point return for the team through the summation of point average predictions for each player individually. This is then compared with the actual FPL gameweek return of the selected team and evaluated. The study found that an MLP can predict the return of each player with an RMSE of approximately 2 points. The study shows the extent of the capability of MLPs in finding ordered patterns in chaotic events.

[ACCEPTED FOR PUBLICATION IN THE DAIS JOURNAL OF SCIENCE 2023] Intercity travel optimization has always been a goal of every government. Such an optimization would allow efficient and economical development. Optimizing travel would facilitate transfer of ideas and resources and promote economic growth. As such, travel can be optimized on two fronts: cost of arranging for that travel and the ease of travel i.e., the length/time of travel for a person to go from any one city to any other. This study proposes a methodology of optimizing travel pathways in any circumstance through genetic algorithm metaheuristics and network theory representations. This study demonstrates the same to optimize inter-city travel between the 25 cities with the highest population in India.