We examine the stationary-state equations for lattices with generalized Markovian dephasing and relaxation. When the Hamiltonian is quadratic, the single-particle correlation matrix has a closed system of equations even in the presence of these two processes. The resulting equations have a vectorized form related to, but distinct from, Lyapunov’s equation. We present an efficient solution that helps to achieve the scaling limit, e.g., of the current decay with lattice length. As an example, we study the super-diffusive-to-diffusive transition in a lattice with long-range hopping and dephasing. The approach enables calculations with up to 104 sites, representing an increase of 10 to 40 times over prior studies. This enables a more precise extraction of the diffusion exponent, enhances agreement with theoretical results, and supports the presence of a phase transition. There is a wide range of problems that have Markovian relaxation, noise, and driving. They include quantum networks for machine-learning-based classification and extended reservoir approaches for transport. The results here will be useful for these classes of problems.
BT - The Journal of Chemical Physics DA - 09 DO - 10.1063/5.0283282 LA - eng N2 -We examine the stationary-state equations for lattices with generalized Markovian dephasing and relaxation. When the Hamiltonian is quadratic, the single-particle correlation matrix has a closed system of equations even in the presence of these two processes. The resulting equations have a vectorized form related to, but distinct from, Lyapunov’s equation. We present an efficient solution that helps to achieve the scaling limit, e.g., of the current decay with lattice length. As an example, we study the super-diffusive-to-diffusive transition in a lattice with long-range hopping and dephasing. The approach enables calculations with up to 104 sites, representing an increase of 10 to 40 times over prior studies. This enables a more precise extraction of the diffusion exponent, enhances agreement with theoretical results, and supports the presence of a phase transition. There is a wide range of problems that have Markovian relaxation, noise, and driving. They include quantum networks for machine-learning-based classification and extended reservoir approaches for transport. The results here will be useful for these classes of problems.
PY - 2025 T2 - The Journal of Chemical Physics TI - Approaching the scaling limit of transport through lattices with dephasing UR - https://doi.org/10.1063/5.0283282 VL - 163 SN - 0021-9606 ER -