Dissipation alters modes of information encoding in small quantum reservoirs near criticality

2024-12-24

AI Summary

This study investigates how dissipation and criticality jointly govern information processing performance in quantum reservoir computing (QRC) implemented on nonlinear open many-body systems. Using a driven-dissipative coupled Kerr oscillator model, we introduce partial information decomposition (PID) to quantify the dynamical evolution of redundant versus synergistic encoding within the quantum reservoir—a first application of PID to QRC. Combining quantum master equation simulations with criticality analysis, we find that near the critical point, information encoding transitions from redundancy-dominated to synergy-dominated; enhanced synergy improves short-term responsiveness, whereas stronger dissipation promotes long-term memory retention. Our work uncovers an intrinsic tripartite relationship among dissipation, critical instability, and information structure, establishing the first information-theoretic principle for functional control of task-optimized QRC architectures.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss. Using Partial Information Decomposition (PID), we examine how different dynamical regimes encode input drive signals in terms of redundancy (information shared by each oscillator) and synergy (information accessible only through their joint observation). Our key results show that, near a critical point marking a dynamical bifurcation, the system transitions from predominantly redundant to synergistic encoding. We further demonstrate that synergy amplifies short-term responsiveness, thereby enhancing immediate memory retention, whereas strong dissipation leads to more redundant encoding that supports long-term memory retention. These findings elucidate how the interplay of instability and dissipation shapes information processing in small quantum systems, providing a fine-grained, information-theoretic perspective for analyzing and designing QRC platforms.

Problem

• Quantum Reservoir Computing
• Many-Body Open Systems
• Nonlinear and Dissipative Interactions

Innovation

• Quantum Reservoir Computing
• Partial Information Decomposition
• Dissipation Effects