# Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing

arXiv: https://arxiv.org/abs/2609.36901 · HTML: https://mlsanity.com/paper/2609.36901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 3 of 3)

- [Deep Learning Reconstruction of Ultra-Short Pulses](https://mlsanity.com/paper/1803.06024/) (2018)
- [Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints](https://mlsanity.com/paper/2509.18483/) (2025)
- [Deep learning and high harmonic generation](https://mlsanity.com/paper/2012.10328/) (2020)

## Abstract (arXiv)

Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and expensive to simulate as system size grows. A paradigmatic case is High-Harmonic Generation~(HHG), where a strong field drives a medium to emit radiation that is highly sensitive to the drive and encodes long-range temporal correlations. We introduce a dissipative quantum reservoir computing~(DQRC) framework that builds a digital twin of such a system, learning its input--output map directly from data while the reservoir---itself a small open quantum system---stays fixed and only a classical readout is trained. We show that a minimal single-qubit reservoir reproduces the HHG response of a substantially larger Ising spin chain, and on a representative benchmark matches and on several metrics surpasses previously reported temporal convolutional and Kolmogorov--Arnold-network models, while using a simpler, physically realizable system. A single fixed reservoir further generalizes across a broad range of drives, indicating that it learns a shared physical response structure rather than memorizing trajectories. These results establish dissipative quantum reservoirs as compact, physically grounded digital twins for nonlinear, memory-dependent quantum dynamics. Code is available at hrefhttps://github.com/AI-and-Quantum-Computing/DQuRChttps://github.com/AI-and-Quantum-Computing/DQuRC.