Fault-tolerant quantum computation requires scaling quantum processors to millions of physical qubits, yet purely superconducting architectures face significant hardware and control overhead. Hybrid multimode piezomechanical systems offer a hardware-efficient alternative by encoding quantum information in dense bosonic acoustic modes. However, stabilizing entanglement in such networks is challenging because continuous measurement and environmental dissipation generate strongly nonlinear driven-dissipative dynamics that are difficult to optimize analytically. Here we present an autonomous artificial-intelligence framework for the design and control of multimode superconducting optomechanical circuits. Using a model-free Recurrent Proximal Policy Optimization agent, the system learns a dynamic control protocol that exploits weak continuous measurement backaction to emulate a squeezed-vacuum environment, stabilizing steady-state remote entanglement with logarithmic negativity EN = 0.506 ± 0.027. In parallel, a Physics-Informed Neural Network incorporating the Lindblad master equation identifies control trajectories near a Liouvillian exceptional point, enabling efficient navigation of the non-Hermitian phase space. This physics-guided optimization reduces the circuit depth of standard quantum parity-check routines by 43.2% relative to conventional compilation. These results demonstrate that integrating machine learning with quantum control can autonomously discover robust strategies for stabilizing entanglement and reducing circuit complexity, providing a scalable pathway toward hardware-efficient quantum networks and distributed quantum information processing.
Ramisetty et al. (Tue,) studied this question.