Model optimisation frameworks are tools or libraries that help improve the efficiency and performance of machine learning models. They automate tasks such as reducing model size, speeding up predictions, and lowering hardware requirements. These frameworks make it easier for developers to deploy models on various devices, including smartphones and embedded systems.
Category: Model Optimisation Techniques
Quantum Error Reduction
Quantum error reduction refers to a set of techniques used to minimise mistakes in quantum computers. Quantum systems are very sensitive to their surroundings, which means they can easily pick up errors from noise, heat or other small disturbances. By using error reduction, scientists can make quantum computers more reliable and help them perform calculations…
Neural Inference Efficiency
Neural inference efficiency refers to how effectively a neural network model processes new data to make predictions or decisions. It measures the speed, memory usage, and computational resources required when running a trained model rather than when training it. Improving neural inference efficiency is important for using AI models on devices with limited power or…
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…
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…
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.
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…