Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

TL;DR

This paper compares physical and logical quantum computations for solving differential equations, demonstrating that logical implementations outperform physical ones due to reduced noise errors, thus supporting fault-tolerant quantum approaches.

Contribution

It provides an experimental validation of the advantages of logical quantum kernels over physical ones in differential equation solving, informing architectural choices.

Findings

Logical quantum kernels perform better than physical kernels on relevant metrics.

Noise-induced errors are reduced in logical implementations, improving performance.

End-to-end application performance confirms the benefits of fault-tolerant quantum computing.

Abstract

We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher quantum resource count, and guide application-informed architectural choices.