Two-dimensional quantum models simulated without full wave function

Researchers at Google Quantum AI have developed a new heuristic approach to accelerate Matrix Product State (MPS) simulations, allowing for estimation of expectation values without the traditionally exponential resource demand of storing the full wave function. The work addresses a computational bottleneck at intermediate energy densities, where simulating both wave function and operator evolution proves costly. This technique rescales MPS results at low bond dimensions using a factor dependent on the fidelity of the MPS wave function, enabling simulations of the two-dimensional Transverse-Field Ising Model on a 7×8 grid with a maximum bond dimension of 4096 on a single A100 GPU, as well as the dynamics of the two-dimensional…