Category: Model Optimisation Techniques

Quantum Data Efficiency

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…

AI-Driven Optimization

AI-driven optimisation uses artificial intelligence to make processes, systems or decisions work better by analysing data and finding the most effective solutions. It often involves machine learning algorithms that can learn from past outcomes and improve over time. This approach saves time, reduces costs and helps achieve better results in complex situations where there are…

Quantum Error Calibration

Quantum error calibration is the process of identifying, measuring, and adjusting for errors that can occur in quantum computers. Because quantum bits, or qubits, are extremely sensitive to their environment, they can easily be disturbed and give incorrect results. Calibration helps to keep the system running accurately by fine-tuning the hardware and software so that…

Quantum Model Optimization

Quantum model optimisation is the process of improving the performance of quantum algorithms or machine learning models that run on quantum computers. It involves adjusting parameters or structures to achieve better accuracy, speed, or resource efficiency. This is similar to tuning traditional models, but it must account for the unique behaviours and limitations of quantum…

Quantum Algorithm Calibration

Quantum algorithm calibration is the process of adjusting and fine-tuning the parameters of a quantum algorithm to ensure it works accurately on a real quantum computer. Because quantum computers are sensitive to errors and environmental noise, careful calibration helps minimise mistakes and improves results. This involves testing, measuring outcomes and making small changes to the…

Neural Inference Optimization

Neural inference optimisation refers to improving the speed and efficiency of running trained neural network models, especially when making predictions or classifications. This process involves adjusting model structures, reducing computational needs, and making better use of hardware to ensure faster results. It is especially important for deploying AI on devices with limited resources, such as…

AI for Optimization

AI for optimisation refers to the use of artificial intelligence techniques to find the best possible solutions to complex problems. This often involves improving processes, saving resources, or increasing efficiency in a system. By analysing data and learning from patterns, AI can help make decisions that lead to better outcomes than traditional methods.

Quantum Circuit Efficiency

Quantum circuit efficiency refers to how effectively a quantum circuit uses resources such as the number of quantum gates, the depth of the circuit, and the number of qubits involved. Efficient circuits achieve their intended purpose using as few steps, components, and time as possible. Improving efficiency is vital because quantum computers are currently limited…

Quantum Model Calibration

Quantum model calibration is the process of adjusting quantum models so their predictions match real-world data or expected outcomes. This is important because quantum systems can behave unpredictably and small errors can quickly grow. Calibration helps ensure that quantum algorithms and devices produce reliable and accurate results, making them useful for scientific and practical applications.

Model Performance Metrics

Model performance metrics are measurements that help us understand how well a machine learning model is working. They show if the model is making correct predictions or mistakes. Different metrics are used depending on the type of problem, such as predicting numbers or categories. These metrics help data scientists compare models and choose the best…