Conditional Seq2Seq model for the time-dependent two-level system
This provides a promising new method for efficiently solving time-dependent equations in quantum systems, though it appears incremental as it builds on existing deep learning architectures.
The authors tackled the problem of solving the time-dependent Schrödinger equation for a two-level system in quantum optics using a conditional Seq2Seq deep learning model, achieving over 90% accuracy in super long-term predictions under random electric fields.
We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the network structure and tuning parameters, above 90 percent accuracy in super long-term predictions can be achieved in the case of random electric fields, which indicates a promising new method to solve the time-dependent equation for two-level systems. By slightly modifying this network, we think that this method can solve the two- or three-dimensional time-dependent Schrodinger equation more efficiently than traditional approaches.