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

TL;DR

This paper introduces gadget reinforcement learning (GRL), a novel approach that automatically constructs composite gates to improve quantum circuit design, addressing hardware constraints and enhancing scalability for complex quantum problems.

Contribution

The paper presents GRL, a new method combining learning and program synthesis to create composite gates, improving quantum circuit accuracy and hardware compatibility.

Findings

Enhanced accuracy in quantum circuit design

Improved hardware compatibility and scalability

Effective for systems up to ten qubits

Abstract

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), which 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.