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

2026-09-29

AI Summary

This study addresses the modeling challenges of many-body quantum systems, which are inherently nonlinear, history-dependent, and computationally expensive to simulate. To this end, we propose a Dissipative Quantum Reservoir Computing (DQRC) framework. This approach leverages a fixed, small-scale open quantum system as a digital twin, requiring only the training of a classical readout layer to directly learn input-output mappings, thereby enabling a physically realizable and compact architecture. Experimental results demonstrate that a minimal reservoir based on a single qubit can reproduce the high-harmonic responses of large-scale systems. Furthermore, the proposed method outperforms conventional models while exhibiting strong generalization capabilities. Overall, this work presents an efficient new paradigm for modeling complex quantum dynamics.

Abstract

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 \href{https://github.com/AI-and-Quantum-Computing/DQuRC}{https://github.com/AI-and-Quantum-Computing/DQuRC}.

Problem

• Quantum Dynamical Systems
• Digital Twin
• High-Harmonic Generation
• Many-body Quantum Systems
• Nonlinear Dynamics

Innovation

• Dissipative Quantum Reservoir Computing
• Digital Twin
• High-Harmonic Generation
• Many-body Quantum Systems
• Open Quantum System