Quantum Simulation

Quantum simulation uses classical or quantum computers to model quantum systems, aiming to understand and predict their behavior, which is often intractable using classical methods alone. Current research focuses on improving simulation efficiency through techniques like surrogate-assisted optimization, variational parameter estimation with parameterized quantum circuits, and advanced algorithms leveraging machine learning (e.g., neural networks, graph neural networks) to accelerate calculations and mitigate errors. These advancements are enabling more accurate and scalable simulations of complex quantum phenomena in materials science, condensed matter physics, and quantum information science, ultimately accelerating scientific discovery and technological development.