OrQstrator: An AI-Powered Framework for Advanced Quantum Circuit Optimization
This work addresses the challenge of optimizing quantum circuits for noisy intermediate-scale quantum hardware, which is incremental as it builds on existing methods like the NISQ Analyzer.
The authors tackled quantum circuit optimization for NISQ-era hardware by proposing OrQstrator, a modular framework using deep reinforcement learning to coordinate multiple optimizers, resulting in reduced circuit depth and gate count with improved fidelity.
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.