# Conditional Seq2Seq model for the time-dependent two-level system

arXiv: https://arxiv.org/abs/2206.02889 · HTML: https://mlsanity.com/paper/2206.02889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Quantum Entanglement in Deep Learning Architectures](https://mlsanity.com/paper/1803.09780/) (2018)
- [Machine learning topological phases in real space](https://mlsanity.com/paper/1901.01963/) (2019)

## Abstract (arXiv)

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.