OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization
Laura Baird, Armin Moin
TL;DR
The paper tackles the challenge of optimizing quantum circuits for NISQ devices by introducing OrQstrator, an AI-powered orchestration framework that coordinates a DRL-based circuit rewriter, a domain-specific numeric optimizer, and a parameterized circuit instantiator to produce hardware-aware, fidelity-aware circuits. It leverages backend metadata and the NISQ Analyzer to tailor optimizations, aiming for substantial reductions in circuit depth and gate counts while improving fidelity. The main contribution is a modular, learned coordination system that surpasses fixed pipelines and individual optimizers, enabling more reliable near-term quantum execution. This approach holds practical significance for improving quantum compilation across diverse hardware targets.
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.