Symmetry-Adapted Eigensolver from Real-Space Sampling of Tensor-Network-Bridged Quantum Circuits
This work proposes a hybrid quantum-classical eigensolver that combines real-space sampling of tensor-network-bridged quantum circuits with symmetric subspace measurements. It efficiently simulates strongly correlated quantum many-body systems by mapping real-space configurations to a reduced symmetric Hilbert space, allowing for accurate energy calculations with high precision, even in small system sizes.