Quantum optimization within lattice gauge theory model on a quantum simulator

Simulating lattice gauge theory (LGT) Hamiltonian and its nontrivial states by programmable quantum devices has attracted numerous attention in recent years. Rydberg atom arrays constitute one of the most rapidly developing arenas for quantum simulation and quantum computing. The Z(2) LGT and topological order has been realized in experiments while the U(1) LGT is being worked hard on the way. States of LGT have local constraints and are fragmented into several winding sectors with topological protection. It is therefore difficult to reach the ground state in target sector for experiments, and it is also an important task for quantum topological memory. Here, we propose a protocol of sweeping quantum annealing (SQA) for searching the ground state among topological sectors. With the quantum Monte Carlo method, we show that this SQA has linear time complexity of size with applications to the antiferromagnetic transverse field Ising model, which has emergent U(1) gauge fields. This SQA protocol can be realized easily on quantum simulation platforms such as Rydberg array and D-wave annealer. We expect this approach would provide an efficient recipe for resolving the topological hindrances in quantum optimization and the preparation of quantum topological state.

Automated summary

Simulating lattice gauge theory Hamiltonian and its nontrivial states using programmable quantum devices has gained significant attention. Rydberg atom arrays have shown promise in quantum simulation and computing. While Z(2) lattice gauge theory and topological order have been realized in experiments, progress is being made towards U(1) lattice gauge theory. This study proposes a sweeping quantum annealing protocol for searching ground states among topological sectors, with linear time complexity for the antiferromagnetic transverse field Ising model. This protocol can be implemented on quantum simulation platforms like Rydberg arrays and D-wave annealer, providing an efficient approach for overcoming topological hindrances in quantum optimization and the preparation of quantum topological states.