An Evolutionary Approach to Optimizing Communication Cost in Distributed Quantum Computation

TL;DR

This paper introduces a genetic algorithm to optimize communication costs in distributed quantum computing, achieving faster results than previous exponential algorithms and outperforming random search methods.

Contribution

The paper presents a novel genetic algorithm approach for optimizing qubit teleportations in distributed quantum circuits, improving efficiency over prior exponential complexity methods.

Findings

Genetic algorithm achieves similar optimization with significantly reduced computation time.

The approach outperforms previous exponential algorithms in speed.

GA is more effective than random search in this context.

Abstract

Distributed quantum computing has been well-known for many years as a system composed of a number of small-capacity quantum circuits. Limitations in the capacity of monolithic quantum computing systems can be overcome by using distributed quantum systems which communicate with each other through known communication links. In our previous study, an algorithm with an exponential complexity was proposed to optimize the number of qubit teleportations required for the communications between two partitions of a distributed quantum circuit. In this work, a genetic algorithm is used to solve the optimization problem in a more efficient way. The results are compared with the previous study and we show that our approach works almost the same with a remarkable speed-up. Moreover, the comparison of the proposed approach based on GA with a random search over the search space verifies the effectiveness of GA.