Tensor-Network-Based Unraveling of Non-Markovian Dynamics in Large Spin Chains via the Influence Martingale Approach
The work develops an efficient algorithm to simulate open quantum system dynamics in large one-dimensional spin chains up to 100 qubits, handling both Markovian and non-Markovian processes. It extends the Tensor Jump Method by using an influence martingale approach with a 'influence radius' concept to capture memory effects in non-Markovian systems more efficiently.