Sep 29, 2026, quant-phJ/K move · Enter open · S save
Abhijit Sen, Bikram Keshari Parida, Shital Chauhan, Mahima Arya+1
Department of Physics and Engineering Physics, Tulane University, New Orleans, Louisiana 70118, USA · Department of Data Science, Dongduk Women’s University, Seoul, South Korea · SSJDWSSSS Government Post Graduate College, Ranikhet 263645, India
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 https://github.com/AI-and-Quantum-Computing/DQuRC.