Category: Artificial Intelligence

Model Retraining Pipelines

Model retraining pipelines are automated processes that regularly update machine learning models using new data. These pipelines help ensure that models stay accurate and relevant as conditions change. By automating the steps of collecting data, processing it, training the model, and deploying updates, organisations can keep their AI systems performing well over time.

Quantum State Optimization

Quantum state optimisation refers to the process of finding the best possible configuration or arrangement of a quantum system to achieve a specific goal. This might involve adjusting certain parameters so that the system produces a desired outcome, such as the lowest possible energy state or the most accurate result for a calculation. It is…

Graph-Based Prediction

Graph-based prediction is a method of using data that is organised as networks or graphs to forecast outcomes or relationships. In these graphs, items like people, places, or things are represented as nodes, and the connections between them are called edges. This approach helps uncover patterns or make predictions by analysing how nodes are linked…

Neural Representation Learning

Neural representation learning is a method in machine learning where computers automatically find the best way to describe raw data, such as images, text, or sounds, using numbers called vectors. These vectors capture important patterns and features from the data, helping the computer understand complex information. This process often uses neural networks, which are computer…

Model Performance Tracking

Model performance tracking is the process of monitoring how well a machine learning or statistical model is working over time. It involves collecting and analysing data about the model’s predictions compared to real outcomes. This helps teams understand if the model is accurate, needs updates, or is drifting from its original performance.

Quantum Model Scaling

Quantum model scaling refers to the process of making quantum computing models larger and more powerful by increasing the number of quantum bits, or qubits, and enhancing their capabilities. As these models get bigger, they can solve more complex problems and handle more data. However, scaling up quantum models also brings challenges, such as maintaining…

Graph Knowledge Propagation

Graph knowledge propagation is a way of spreading information through a network of connected items, called nodes, based on their relationships. Each node can share what it knows with its neighbours, helping the whole network learn more about itself. This method is used in computer science and artificial intelligence to help systems understand complex structures,…

Quantum Feature Mapping

Quantum feature mapping is a technique used in quantum computing to transform classical data into a format that can be processed by a quantum computer. It involves encoding data into quantum states so that quantum algorithms can work with the information more efficiently. This process can help uncover patterns or relationships in data that may…

Neural Activation Analysis

Neural activation analysis is the process of examining which parts of a neural network are active or firing in response to specific inputs. By studying these activations, researchers and engineers can better understand how a model processes information and makes decisions. This analysis is useful for debugging, improving model performance, and gaining insights into what…

Quantum Noise Mitigation

Quantum noise mitigation refers to techniques used to reduce or correct errors that occur in quantum computers due to unwanted disturbances. These disturbances, known as noise, can come from the environment, imperfect hardware, or interference during calculations. By applying noise mitigation, quantum computers can perform more accurate computations and produce more reliable results.