Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware

Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large-scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing reinforcement learning-based methods for circuit design lose accuracy when restricted to hardware-native gates and device-level compilation. Here, we introduce gadget reinforcement learning (GRL) that combines learning with program synthesis to automatically construct composite gates that expand the action space while respecting hardware constraints. We show that this approach improves accuracy, hardware compatibility, and scalability for transverse-field Ising and quantum chemistry problems, reaching systems of up to ten qubits within realistic computational budgets. This framework demonstrates how learned, reusable circuit building blocks can guide the co-design of algorithms and hardware for quantum processors. Designing quantum circuits is challenging due to the exponential growth of the state space. The authors introduce gadget reinforcement learning, which combines reinforcement learning with program synthesis to enhance quantum circuit exploration, demonstrating improved accuracy and scalability in complex tasks, thus bridging algorithmic design and practical implementation for hardware-specific optimizations.