Category: AI Infrastructure

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…

Model Inference Frameworks

Model inference frameworks are software tools or libraries that help run trained machine learning models to make predictions on new data. They handle tasks like loading the model, preparing input data, running the calculations, and returning results. These frameworks are designed to be efficient and work across different hardware, such as CPUs, GPUs, or mobile…

Cloud Resource Optimization

Cloud resource optimisation is the process of managing and adjusting the use of cloud services to achieve the best performance at the lowest possible cost. It involves analysing how much computing power, storage, and network resources are being used and making changes to avoid waste or unnecessary expenses. This can include resizing virtual machines, shutting…

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…

Cloud-Native Monitoring

Cloud-native monitoring is the process of observing and tracking the performance, health, and reliability of applications built to run on cloud platforms. It uses specialised tools to collect data from distributed systems, containers, and microservices that are common in cloud environments. This monitoring helps teams quickly detect issues, optimise resources, and ensure that services are…

Model Deployment Metrics

Model deployment metrics are measurements used to track the performance and health of a machine learning model after it has been put into use. These metrics help ensure the model is working as intended, making accurate predictions, and serving users efficiently. Common metrics include prediction accuracy, response time, system resource usage, and the rate of…

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 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…