Category: Model Optimisation Techniques

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…

Quantum Noise Handling

Quantum noise handling refers to the methods and techniques used to reduce or manage unwanted disturbances in quantum systems. These disturbances, called quantum noise, can interfere with the accuracy of quantum computers and other quantum devices. Effective noise handling is essential for reliable quantum operations, as even small errors can disrupt calculations and communication.

Quantum Circuit Scaling

Quantum circuit scaling refers to the process of increasing the size and complexity of quantum circuits, which are sequences of operations performed on quantum bits, or qubits. As quantum computers grow more powerful, they can handle larger circuits to solve more complex problems. However, scaling up circuits introduces challenges such as maintaining qubit quality and…

Quantum Algorithm Optimization

Quantum algorithm optimisation is the process of improving quantum algorithms so they use fewer resources, run faster, or solve problems more accurately. This often involves reducing the number of quantum operations needed and making the best use of available quantum hardware. The goal is to make quantum computing more practical and efficient for real-world tasks.