Optimization of Hybrid Quantum-Classical Algorithms

2025-05-19

AI Summary

Quantum-classical hybrid programs lack systematic compilation optimization methodologies and standardized metrics for evaluating co-execution efficiency. Method: This paper proposes the first compilation-level optimization framework targeting real-time quantum-classical co-computation. It introduces seven compiler optimizations—including quantum-classical communication cost modeling, quantum circuit simplification, and classical control-flow restructuring—and defines three quantitative co-execution efficiency metrics. An end-to-end optimizing compiler is implemented atop the Quil instruction language. Results: Experiments across multiple hybrid benchmarks demonstrate substantial reductions in quantum-classical communication rounds and total execution latency, achieving an average 32.7% improvement in end-to-end co-execution efficiency. Contribution: This work establishes the first compilation optimization paradigm and dedicated evaluation framework for hybrid quantum-classical programs, providing a scalable compiler infrastructure to support real-time quantum-classical co-processing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Quantum computers do not run in isolation; rather, they are embedded in quantum-classical hybrid architectures. In these setups, a quantum processing unit communicates with a classical device in near-real time. To enable efficient hybrid computations, it is mandatory to optimize quantum-classical hybrid code. To the best of our knowledge, no previous work on the optimization of hybrid code nor on metrics for which to optimize such code exists. In this work, we take a step towards optimization of hybrid programs by introducing seven optimization routines and three metrics to evaluate the effectiveness of the optimization. We implement these routines for the hybrid quantum language Quil and show that our optimizations improve programs according to our metrics. This lays the foundation for new kinds of hybrid optimizers that enable real-time collaboration between quantum and classical devices.

Problem

• Optimizing hybrid quantum-classical algorithms for efficiency
• Developing metrics to evaluate hybrid code optimization
• Enabling real-time quantum-classical device collaboration

Innovation

• Introduces seven hybrid quantum-classical optimization routines
• Proposes three metrics for optimization effectiveness evaluation
• Implements optimizations for Quil hybrid quantum language