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

2025-07-13

AI Summary

To address the challenge of hardware noise and architectural constraints in the Noisy Intermediate-Scale Quantum (NISQ) era—where conventional quantum circuit optimization strategies fail to coordinate effectively—this paper proposes a deep reinforcement learning (DRL)-driven, structure-adaptive co-optimization framework. The framework employs an AI-powered scheduler to dynamically orchestrate three complementary modules: domain-specific gate-level rewriting, local gate fusion with numerical optimization, and hardware-aware parametrized template instantiation, augmented by a NISQ Analyzer for backend constraint adaptation. It is the first work to tightly integrate DRL with parametrized templates and hardware feature modeling, enabling cross-platform circuit translation. Experimental evaluation across multiple real quantum hardware backends demonstrates significant improvements: average circuit depth reduction of 28.6%, gate count reduction of 22.4%, and expected fidelity gain of 12.3%, consistently outperforming state-of-the-art methods.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Optimizing quantum circuits for NISQ era using AI
• Reducing circuit depth and gate count via DRL
• Adapting compilation to backend constraints and fidelity

Innovation

• Deep Reinforcement Learning for quantum circuit optimization
• Modular framework with three complementary optimizers
• Backend-aware performance features for hardware adaptation