Optimization of Hybrid Quantum-Classical Algorithms

TL;DR

This paper introduces seven optimization routines and three metrics for improving hybrid quantum-classical programs, demonstrating their effectiveness in enhancing program performance and laying groundwork for real-time hybrid system optimizers.

Contribution

It presents the first set of optimization routines and metrics specifically designed for hybrid quantum-classical code, advancing the field of quantum computing optimization.

Findings

Optimizations improve hybrid program performance according to new metrics.

Implementation on Quil language demonstrates practical effectiveness.

Lays foundation for real-time hybrid quantum-classical optimizers.

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.