Posts by Luka Tsabadze
Running Variational Quantum Eigensolver with Qiskit Aer on AMD Instinct
- 29 May 2026
Quantum computing offers a fundamentally different approach to computational problems by leveraging quantum mechanical properties such as superposition and entanglement. Unlike a classical bit, which is always 0 or 1, a qubit can exist in a superposition of both, and in principle this gives a significant resource advantage: \(n\) qubits represent a state that would otherwise require \(2^n\) complex numbers on a classical computer. However, current quantum hardware is still in its early stages - noise and limited qubit counts constrain the scale of problems it can handle reliably. GPU-accelerated simulators efficiently emulate quantum circuits on classical hardware, though they inherit the same exponential memory cost and become impractical past a few dozen qubits. Of course, any problem whose quantum circuit can be fully simulated on classical hardware can also be handled with other methods that avoid the simulation overhead, but the real value of circuit simulation is the opportunity to develop, validate, and benchmark quantum algorithms in a controlled setting where exact solutions are known, so that the same algorithms can be trusted on future hardware tackling problems that remain intractable at scale today.
Fine-Tuning AI Surrogate Models for Physics Simulations with Walrus on AMD Instinct GPU Accelerators
- 06 March 2026
Physics simulations are used for studying complex systems and are essential where experiments are difficult, expensive, or impossible. In our context, a simulation means numerically solving mathematical equations that are believed to describe a physical system and evolving them forward in time on a computer. They enable controlled exploration of physical behavior for science and engineering, but at a high computational cost, which in most cases increases rapidly with scale. Our focus is on continuum dynamics, where the system is represented by fields such as density, velocity, or temperature, defined on a grid and evolving over time. High-resolution physics simulations are slow to run, sensitive to numerical error and impractical for large parameter spaces. Surrogate models address these limitations by learning to approximate simulation dynamics directly from data. Once trained, they can produce fast predictions at a fraction of the cost, giving researchers the ability to rapidly explore parameter space and generate long rollouts.
Training AI Weather Forecasting Models on AMD Instinct
- 10 November 2025
Weather forecasting is one of the most computationally intensive scientific challenges and an essential societal need. Predicting extreme weather events, agricultural and energy planning and daily forecasts all require accurate weather predictions. Traditionally, Numerical Weather Prediction (NWP) has served as the foundation of weather forecasting by solving complex physical equations that require significant computational power. However, recent advances in machine learning have led to the development of alternative prediction models that reduce computational costs by orders of magnitude, while either maintaining or improving accuracy in forecasts. Models like GenCast [1], Pangu-Weather [2], Aurora [3] and others have shown promising results in this area (see the WeatherBench [4] scorecard). Running inference on these models using AMD GPUs is straightforward, as highlighted in our recent blog post: Running SOTA AI-based Weather Forecasting models on AMD Instinct.
Running SOTA AI-based Weather Forecasting models on AMD Instinct
- 18 September 2025
Weather Forecasting is a complex scientific problem where immense progress has been made through the Numerical Weather Prediction (NWP) approach using computational fluid dynamics-based models. Forecasting is usually done in three stages: a data assimilation stage where all available data streams at the time \(t\) (sometimes previous times can be used to improve this estimate) are used to estimate the current 3D state of the atmosphere \( S_{t}\) (surface and atmosphere), as parameterized by a number of variables at the current time \( t\), a forecasting stage where the state \(\hat{S}_{t + \delta t}\) for a later time \( t+ \delta t\) (i.e., all the variables at this later time) are forecasted, and a downstream stage where the forecasted state at time \(t + \delta t\) is used to estimate weather variables at more specific times and locations.