Quantum-Enhanced Deterministic Inference of $k$-Independent Set Instances on Neutral Atom Arrays

TL;DR

This paper introduces a deterministic error mitigation method for noisy quantum measurements on neutral atom arrays, enabling cost-effective benchmarking of quantum devices against classical algorithms for the $k$-independent set problem.

Contribution

The paper presents a novel shot-level inference procedure, DEM, that accounts for noise and reduces classical postprocessing costs in quantum benchmarking.

Findings

DEM reduces postprocessing overhead compared to classical inference.

Experimental results validate the scaling of DEM with system size and error rate.

One hour of classical computation matches quantum experiments with up to 450 atoms.

Abstract

Noisy quantum annealing experiments on Rydberg atom arrays produce measurement outcomes that deviate from ideal distributions, complicating performance evaluation. To enable a data-driven benchmarking methodology for quantum devices that accounts for both solution quality and the classical computational cost of inference from noisy measurements, we introduce deterministic error mitigation (DEM), a shot-level inference procedure informed by experimentally characterized noise. We demonstrate this approach using the decision version of the $k$-independent set problem. Within a Hamming-shell framework, the DEM candidate volume is governed by the binary entropy of the bit-flip error rate, yielding an entropy-controlled classical postprocessing cost. Using experimental measurement data, DEM reduces postprocessing overhead relative to classical inference baselines. Numerical simulations and experimental results from neutral atom devices validate the predicted scaling with system size and error rate. These scalings indicate that one hour of classical computation on an Intel i9 processor corresponds to neutral atom experiments with up to $N=250-450$ atoms at effective error rates, enabling a direct, cost-based comparison between noisy quantum experiments and classical algorithms.