• Major Technological Contributions of the QERC Framework. • The proposed quantum entangled reservoir computing (QERC) framework introduces several key technological advancements that distinguish it from conventional data-driven and model-based control strategies: • Unitary “Glass-Box” Stability Guarantee. • Unlike conventional black-box neural architectures, the proposed framework employs a transparent, physics-inspired quantum reservoir constructed using analytically derived unitary entanglement matrices. By design, all eigenvalues lie strictly on the unit circle, ensuring inherent Lyapunov stability and energy-conserving state evolution. This guarantee bounded internal dynamics and provides a mathematically grounded safety assurance, which is critical for real-time control in safety–critical power systems. • Unified Multi-Objective Control Substrate. • QERC introduces a single-loop control architecture capable of simultaneously addressing five tightly coupled objectives: voltage regulation, frequency stabilization, state-of-charge (SOC) estimation, harmonic compensation, and battery loss minimization. This unified framework eliminates the need for cascaded or hierarchical controllers, thereby avoiding control conflicts and enabling globally optimal system behaviour under multi-physics disturbances. • Ultra-Fast Virtual Active Power Filter (V-APF) • Leveraging its high-dimensional dynamic reservoir and deterministic inference latency (10 μs), the QERC controller functions as an embedded virtual active power filter. It effectively mitigates harmonic distortions by dynamically cancelling non-linear load components, achieving Total Harmonic Distortion (THD) reduction from >5% to <3.5% even under extreme storm-induced intermittency, with compensation extending up to the 7th harmonic order. • Implicit Neural SOC Observer. • The framework inherently embeds a nonlinear SOC estimation mechanism within the reservoir dynamics, eliminating the need for separate estimation modules. By fusing battery current, terminal voltage, and thermal behaviour, the QERC achieves a highly accurate SOC estimation with an RMSE of 0.21%, significantly outperforming conventional approaches such as LSTM-based observers and Kalman filtering techniques. • Regularized Operational Loss Suppression. • Through a physics-constrained ridge regression training strategy (λ ≈ 0.85), the QERC framework explicitly penalizes high-frequency control oscillations. This results in smoother control actions, reduced battery micro-cycling, and significant mitigation of thermal stress. Consequently, peak battery current is reduced from 920 A to 610 A, leading to a 35.9% reduction in cumulative energy losses, thereby enhancing both efficiency and battery lifespan. This paper introduces quantum entangled reservoir computing (QERC), a unified glass box control paradigm for battery energy storage systems (BESS) that replaces opaque black box learning with a transparent, physics inspired framework. Unlike randomly initialized neural networks or conventional Echo state networks, QERC employs a fixed complex valued reservoir constructed using unitary entanglement matrices, ensuring inherent Lyapunov stability and energy conserving dynamic behaviour analogous to quantum chaotic systems. Implemented in MATLAB/Simulink on a heavily modified IEEE 14-bus renewable microgrid, a rigorous benchmark selected to validate topological robustness under severe solar, wind intermittency and non-linear dynamic load changes, the proposed architecture simultaneously addresses five coupled objectives, voltage regulation, frequency support, state of charge (SOC) estimation, harmonic mitigation, and battery loss minimization, within a single coherent computational substrate, thereby avoiding fragmented cascaded control loops. By leveraging high bandwidth internal dynamics and deterministic 10 μs computational latency, QERC operates as an embedded virtual active power filter (V-APF), reducing grid total harmonic distortion (THD) from above 5% to below 3.5% under dynamic loading conditions. A regularized single shot ridge regression training strategy penalizes high frequency control jitter, suppressing battery micro-cycling and yielding approximately 35.9% reduction in battery operational losses. Performance evaluation demonstrates voltage regulation within ± 0.028p.u, frequency containment within ± 0.14 Hz, and highly accurate SOC estimation with 0.21% RMSE, by consistently outperforming a comprehensive suite of state of art baselines, spanning predictive (MPC), heuristic (FA-PSO), classical (VSG), and deep learning (RL) paradigms. These results validate the broad applicability of QERC. These results establish QERC as interpretable and computationally efficient control framework that bridges high performance AI methods with the stringent stability and real-time requirements of modern power microgrids.
Rawat et al. (2026) studied this question.