A Simplex-Inspired Architecture for Integrating Quantum Capabilities into Cyber-Physical Systems
For designers of safety-critical cyber-physical systems, this work provides a method to integrate quantum computing while maintaining safety guarantees, though it is an incremental combination of existing techniques.
The paper proposes a hybrid classical-quantum system identification framework using a Simplex architecture that dynamically switches between quantum-assisted and classical Gaussian process models to balance performance and safety in real-time cyber-physical systems. Experiments on a Continuous Stirred-Tank Reactor benchmark demonstrate a controllable trade-off between performance and safety.
Cyber-physical systems require accurate and reliable system models to ensure safe and efficient operation. Classical Gaussian Process Regression (GPR) provides uncertainty-aware predictions but suffers from high computational complexity, which limits its scalability in real-time applications. Quantum-assisted Gaussian process models reduce complexity in inference, but their practical use is constrained by noise and stability concerns in safety-critical environments. In this paper, we propose a hybrid classical-quantum system identification framework based on a Simplex architecture. The framework combines Quantum-Assisted Hilbert-Space Gaussian Process Regression (QA-HSGPR) as a high-performance module and classical GPR as a high-assurance module. A runtime monitor evaluates system safety and dynamically switches between the two models. Experiments on a Continuous Stirred-Tank Reactor benchmark demonstrate that the proposed framework enables a controllable trade-off between performance and safety for real-time cyber-physical systems.