Category: Model Optimisation Techniques

Neural Weight Optimization

Neural weight optimisation is the process of adjusting the strength of connections between nodes in a neural network so that it can perform tasks like recognising images or translating text more accurately. These connection strengths, called weights, determine how much influence each piece of information has as it passes through the network. By optimising these…

Model Inference Scaling

Model inference scaling refers to the process of increasing a machine learning model’s ability to handle more requests or data during its prediction phase. This involves optimising how a model runs so it can serve more users at the same time or respond faster. It often requires adjusting hardware, software, or system architecture to meet…

Quantum Algorithm Efficiency

Quantum algorithm efficiency measures how quickly and effectively a quantum computer can solve a problem compared to a classical computer. It focuses on the resources needed, such as the number of steps or qubits required, to reach a solution. Efficient quantum algorithms can solve specific problems much faster than the best-known classical methods, making them…

Quantum Error Handling

Quantum error handling is the process of detecting and correcting mistakes that occur in quantum computers due to noise or interference. Because quantum bits, or qubits, are very sensitive, even small environmental changes can cause errors in calculations. Effective error handling is crucial to ensure quantum computers provide reliable results and can run complex algorithms…

Adaptive Learning Rates

Adaptive learning rates are techniques used in training machine learning models where the rate at which the model learns changes automatically during the training process. Instead of using a fixed learning rate, the algorithm adjusts the rate depending on how well the model is improving. This helps the model learn more efficiently, making faster progress…

Data Flow Optimization

Data flow optimisation is the process of improving how data moves and is processed within a system, such as a computer program, network, or business workflow. The main goal is to reduce delays, avoid unnecessary work, and use resources efficiently. By streamlining the path that data takes, organisations can make their systems faster and more…

Quantum Circuit Design

Quantum circuit design is the process of creating step-by-step instructions for quantum computers. It involves arranging quantum gates, which are the building blocks for manipulating quantum bits, in a specific order to perform calculations. The aim is to solve a problem or run an algorithm using the unique properties of quantum mechanics. Designing a quantum…

Federated Learning Scalability

Federated learning scalability refers to how well a federated learning system can handle increasing numbers of participants or devices without a loss in performance or efficiency. As more devices join, the system must manage communication, computation, and data privacy across all participants. Effective scalability ensures that the learning process remains fast, accurate, and secure, even…

Inference Acceleration Techniques

Inference acceleration techniques are methods used to make machine learning models, especially those used for predictions or classifications, run faster and more efficiently. These techniques reduce the time and computing power needed for a model to process new data and produce results. Common approaches include optimising software, using specialised hardware, and simplifying the model itself.