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

TL;DR

OrQstrator is a modular, AI-driven framework that uses deep reinforcement learning to optimize quantum circuits for NISQ devices by intelligently coordinating multiple specialized optimizers based on circuit and hardware features.

Contribution

It introduces a novel orchestration engine that combines multiple quantum circuit optimizers using DRL to enhance circuit quality tailored to hardware constraints.

Findings

Improved circuit depth and gate count reduction.

Enhanced hardware-aware transpilation.

Effective coordination of multiple optimization modules.

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.