Research on Progress of Quantum Computing Simulation of Physical Systems

Quantum computing, as a forefront field in quantum technology, has made significant progress in simulating physical systems, yet it still faces technical challenges such as hardware noise and quantum errors. This review discusses the latest advancements in quantum computing for simulating physical systems, with a focus on the application of quantum-classical hybrid algorithms and error mitigation techniques, exploring their strengths and limitations across various physical systems. The review covers the simulation of molecular systems using superconducting quantum computers, many-body problems in condensed matter systems, solving equations in complex fluid dynamics, and applications in astrophysics and high-energy physics. For molecular systems, variational quantum algorithms (VQE) are widely used to solve the ground state energy of multi-electron systems, with error mitigation methods improving simulation accuracy. In condensed matter systems, quantum computing has shown high precision and efficiency in simulating strongly correlated spin models, such as the Heisenberg and Ising models, achieving unprecedented accuracy in larger spin chain simulations. In the field of fluid dynamics, research indicates that quantum-classical hybrid algorithms can accelerate the solution of the Navier-Stokes equations to some extent, providing new tools for future fluid dynamics studies. In astrophysical simulations, quantum computing has been used to study the properties of black holes and dark matter, demonstrating potential exponential acceleration, which offers new possibilities for understanding physical phenomena under extreme conditions in the universe. In high-energy physics, quantum computing shows promising applications in solving problems like the Schwinger model and has begun exploring the potential of quantum machine learning in analyzing high-energy experimental data. This review provides a comprehensive perspective on the applications of quantum computing in simulating various physical systems, and outlines future directions and technical challenges.