{"ID":2895254,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09682","arxiv_id":"2507.09682","title":"OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization","abstract":"We propose a novel approach, OrQstrator, which is a modular framework for conducting quantum circuit optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. Our framework is powered by Deep Reinforcement Learning (DRL). Our orchestration engine intelligently selects among three complementary circuit optimizers: A DRL-based circuit rewriter trained to reduce depth and gate count via learned rewrite sequences; a domain-specific optimizer that performs efficient local gate resynthesis and numeric optimization; a parameterized circuit instantiator that improves compilation by optimizing template circuits during gate set translation. These modules are coordinated by a central orchestration engine that learns coordination policies based on circuit structure, hardware constraints, and backend-aware performance features such as gate count, depth, and expected fidelity. The system outputs an optimized circuit for hardware-aware transpilation and execution, leveraging techniques from an existing state-of-the-art approach, called the NISQ Analyzer, to adapt to backend constraints.","short_abstract":"We propose a novel approach, OrQstrator, which is a modular framework for conducting quantum circuit optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. Our framework is powered by Deep Reinforcement Learning (DRL). Our orchestration engine intelligently selects among three complementary circuit optimizers...","url_abs":"https://arxiv.org/abs/2507.09682","url_pdf":"https://arxiv.org/pdf/2507.09682v2","authors":"[\"Laura Baird\",\"Armin Moin\"]","published":"2025-07-13T15:38:39Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\",\"cs.ET\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}