Automating Variational Quantum Sensing through Reinforcement-Learned Circuit Structures
Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ans\"atze while using fewer entangling gates, establishing AutoQSense as a resource-aware approach to adaptive and hardware-compatible quantum sensing.