Graph signal modelling is the process of representing and analysing data that is spread out over a network or graph, such as social networks, transport systems or sensor grids. Each node in the graph has a value or signal, and the edges show how the nodes are related. By modelling these signals, we can better…
AI-powered analytics uses artificial intelligence to automatically examine large amounts of data and find important patterns or trends. It helps people and organisations understand what is happening and make better decisions by quickly processing information that would take humans much longer to analyse. By using machine learning and automation, AI-powered analytics can provide deeper insights…
Quantum data efficiency refers to how effectively quantum computers use data during calculations. It focuses on minimising the amount of data and resources needed to achieve accurate results. This is important because quantum systems are sensitive and often have limited capacity, so making the best use of data helps improve performance and reduce errors. Efficient…
Graph predictive modelling is a type of data analysis that uses the connections or relationships between items to make predictions about future events or unknown information. It works by representing data as a network or graph, where items are shown as points and their relationships as lines connecting them. This approach is especially useful when…
Quantum algorithm analysis is the process of examining and understanding how algorithms designed for quantum computers work, how efficient they are, and what problems they can solve. It involves comparing quantum algorithms to classical ones to see if they offer speed or resource advantages. This analysis helps researchers identify which tasks can benefit from quantum…
Quantum model analysis is a way of using mathematical models based on quantum physics to understand and predict how very small particles, like atoms and electrons, behave. These models help scientists and engineers make sense of complex systems that classical physics cannot explain. By analysing quantum models, researchers can design new materials, medicines, and technology…
Graph knowledge modelling is a way to organise and represent information using nodes and relationships, much like a map of connected points. Each node stands for an item or concept, and the links show how these items are related. This approach helps computers and people understand complex connections within data, making it easier to search,…
Model retraining metrics are measurements used to evaluate how well a machine learning model performs after it has been updated with new data. These metrics help decide if the retrained model is better, worse, or unchanged compared to the previous version. Common metrics include accuracy, precision, recall, and loss, depending on the specific task.
Quantum data scaling refers to the process of managing, transforming, and adapting data so it can be effectively used in quantum computing systems. This involves converting large or complex datasets into a format suitable for quantum algorithms, often by compressing or encoding the data efficiently. The goal is to ensure that quantum resources are used…
Graph predictive analytics is a method that uses networks of connected data, called graphs, to forecast future outcomes or trends. It examines how entities are linked and uses those relationships to make predictions, such as identifying potential risks or recommending products. This approach is often used when relationships between items, people, or events provide valuable…