ATOM: An Efficient Topology Adaptive Algorithm for Minor Embedding in Quantum Computing

TL;DR

This paper introduces ATOM, an efficient algorithm for minor embedding in quantum annealing that adaptively constructs a subgraph of the hardware graph, significantly reducing embedding time while maintaining quality.

Contribution

The paper proposes ATOM, a novel adaptive topology-based algorithm for minor embedding that improves speed without sacrificing embedding quality in quantum annealing.

Findings

ATOM achieves faster embedding times compared to existing methods.

ATOM maintains high-quality embeddings comparable to state-of-the-art techniques.

Experimental results validate the efficiency and effectiveness of ATOM.

Abstract

Quantum annealing (QA) has emerged as a powerful technique to solve optimization problems by taking advantages of quantum physics. In QA process, a bottleneck that may prevent QA to scale up is minor embedding step in which we embed optimization problems represented by a graph, called logical graph, to Quantum Processing Unit (QPU) topology of quantum computers, represented by another graph, call hardware graph. Existing methods for minor embedding require a significant amount of running time in a large-scale graph embedding. To overcome this problem, in this paper, we introduce a novel notion of adaptive topology which is an expandable subgraph of the hardware graph. From that, we develop a minor embedding algorithm, namely Adaptive TOpology eMbedding (ATOM). ATOM iteratively selects a node from the logical graph, and embeds it to the adaptive topology of the hardware graph. Our experimental results show that ATOM is able to provide a feasible embedding in much smaller running time than that of the state-of-the-art without compromising the quality of resulting embedding.