Reinforcement Learning for Quantum Sensor Circuit Design

Quantum sensors promise revolutionary improvements in precision measurement, but their optimization remains one of the grand challenges in quantum engineering. This lecture introduces a Reinforcement Learning (RL)-driven framework for automating the design of quantum sensor circuits, as proposed in Dr. Kumar’s NSF-funded project “RL-QSC: Reinforcement Learning for the Optimal Design of Programmable Quantum Sensor Circuits.”

The session begins with a conceptual primer on quantum sensing—covering entanglement, coherence, and Quantum Fisher Information (QFI)—and the limitations of classical optimization techniques. Dr. Kumar then demonstrates how RL agents can learn to configure quantum circuits by maximizing sensitivity while minimizing decoherence. Real-world case studies using Deep Q-Learning, Policy Gradient, and Actor–Critic methods will show how RL enables adaptive tuning of gate parameters and entanglement structures under realistic noise models.

The talk bridges quantum physics and AI, emphasizing how hybrid RL-quantum architectures form the foundation for intelligent autonomous laboratories. Participants will see how this paradigm integrates with cloud-based quantum platforms, experiment simulators, and reinforcement learning toolkits. The lecture concludes with current challenges—scalability, explainability, and robustness.