Approximate combinatorial optimization with Rydberg atoms: the barrier of interpretability

TL;DR

This paper investigates the interpretability challenges of using Rydberg atom-based quantum computers for solving general graph optimization problems, highlighting the limitations of current embedding and correction strategies.

Contribution

It evaluates two interpretation strategies for Rydberg atom embeddings, revealing fundamental limitations that hinder scalable, high-quality solutions for complex graphs.

Findings

Closest embedding correction yields high quality but is exponentially costly.

Ignoring defective regions is polynomial but degrades solution quality.

Defect scaling conflicts with known approximability conjectures.

Abstract

Analog quantum computing with Rydberg atoms is seen as an avenue to solve hard graph optimization problems, because they naturally encode the Maximum Independent Set (MIS) problem on Unit-Disk (UD) graphs, a problem that admits rather efficient approximation schemes on classical computers. Going beyond UD-MIS to address generic graphs requires embedding schemes, typically with chains of ancilla atoms, and an interpretation algorithm to map results back to the original problem. However, interpreting approximate solutions obtained with realistic quantum computers proves to be a difficult problem. As a case study, we evaluate the ability of two interpretation strategies to correct errors in the recently introduced Crossing Lattice embedding. We find that one strategy, based on finding the closest embedding solution, leads to very high qualities, albeit at an exponential cost. The second strategy, based on ignoring defective regions of the embedding graph, is polynomial in the graph size, but it leads to a degradation of the solution quality which is prohibitive under realistic assumptions on the defect generation. Moreover, more favorable defect scalings lead to a contradiction with well-known approximability conjectures. Therefore, it is unlikely that a scalable and generic improvement in solution quality can be achieved with Rydberg platforms -- thus moving the focus to heuristic algorithms.