Category: Model Optimisation Techniques

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.

Model Optimization Frameworks

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.

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.