Graph knowledge extraction is the process of identifying and organising relationships between different pieces of information, usually by representing them as nodes and connections in a graph structure. This method helps to visualise and analyse how various elements, such as people, places, or concepts, are linked together. It is often used to turn unstructured text…
Category: Artificial Intelligence
Quantum Circuit Optimization
Quantum circuit optimisation is the process of improving the structure and efficiency of quantum circuits, which are the sequences of operations run on quantum computers. By reducing the number of gates or simplifying the arrangement, these optimisations help circuits run faster and with fewer errors. This is especially important because current quantum hardware has limited…
Neural Feature Analysis
Neural feature analysis is the process of examining and understanding the patterns or characteristics that artificial neural networks use to make decisions. It involves identifying which parts of the input data, such as pixels in an image or words in a sentence, have the most influence on the network’s output. By analysing these features, researchers…
Quantum Model Efficiency
Quantum model efficiency refers to how effectively a quantum computing model uses its resources, such as qubits and computational steps, to solve a problem. It measures how much faster or more accurately a quantum system can perform a task compared to traditional computers. Improving quantum model efficiency is important to make quantum computing practical and…
Graph-Based Analytics
Graph-based analytics is a way of analysing data by representing it as a network of points and connections. Each point, called a node, represents an object such as a person, place, or device, and the connections, called edges, show relationships or interactions between them. This approach helps uncover patterns, relationships, and trends that might not…
AI for Forecasting
AI for Forecasting uses computer systems that learn from data to predict what might happen in the future. These systems can spot patterns and trends in large amounts of information, helping people make better decisions. Forecasting with AI can be used in areas like business, weather prediction, and healthcare planning.
Quantum Data Analysis
Quantum data analysis is the process of using quantum computing methods to examine and interpret large or complex sets of data. Unlike traditional computers, quantum computers use quantum bits, which can exist in multiple states at once, allowing them to process certain types of information much more efficiently. This approach aims to solve problems in…
Model Performance Automation
Model Performance Automation refers to the use of software tools and processes that automatically monitor, evaluate, and improve the effectiveness of machine learning models. Instead of manually checking if a model is still making accurate predictions, automation tools can track model accuracy, detect when performance drops, and even trigger retraining without human intervention. This approach…
Quantum Noise Analysis
Quantum noise analysis studies the unpredictable disturbances that affect measurements and signals in quantum systems. This type of noise arises from the fundamental properties of quantum mechanics, making it different from typical electrical or thermal noise. Understanding quantum noise is important for improving the accuracy and reliability of advanced technologies like quantum computers and sensors.
Graph Predictive Systems
Graph predictive systems are computer models that use graphs to represent relationships between different items and then predict future events, trends, or behaviours based on those relationships. In these systems, data is organised as nodes (representing entities) and edges (showing how those entities are connected). By analysing the connections and patterns in the graph, the…