OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization

• URL: http://arxiv.org/abs/2507.09682v2
• Date: Thu, 24 Jul 2025 06:16:38 GMT
• Title: OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization
• Authors: Laura Baird, Armin Moin,
• Abstract summary: OrQstrator is a modular framework for conducting quantum circuit optimization in the Noisy Intermediate-Scale Quantum (NISQ) era.<n>Our framework is powered by Deep Reinforcement Learning (DRL)
• Score: 3.9134031118910264
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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