Dissipative quantum reservoirs model complex quantum system responses
Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing
Artificial Intelligence
Summary
Modeling how complex quantum systems react to inputs is very hard because their behavior depends on past states and involves complicated interactions. The authors present a method that uses a small quantum system, called a dissipative quantum reservoir, to learn how a bigger quantum system behaves just by looking at input and output data. This small quantum system does not change internally; only a classical part that reads its output is trained. Their approach performs as well or better than existing machine learning models on a standard challenge involving high-harmonic generation, using a simpler and physically realistic setup.
What this means in practice
- •For quantum hardware engineers: Characterize and replicate complex quantum material responses using small, physically implementable quantum reservoir models.
- •For signal processing teams: Use compact quantum-inspired reservoir computing to analyze and predict nonlinear memory-dependent signal transformations.
Authors
Abhijit Sen, Bikram Keshari Parida, Shital Chauhan, Mahima Arya, Denys I. Bondar
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}.