Probing graph topology from local quantum measurements

TL;DR

This paper demonstrates that global properties of a quantum network can be inferred from local measurements, enabling network diagnostics and intrusion detection in quantum Internet infrastructures.

Contribution

It introduces a novel method combining local quantum measurements with machine learning-inspired techniques to reveal global network properties.

Findings

Global network properties can be inferred from local measurements.

A malicious agent can extract structural information using local quantum states.

The approach enables new strategies for intrusion detection in quantum networks.

Abstract

We show that global properties of an unknown quantum network, such as the average degree, hub density, and the number of closed paths of fixed length, can be inferred from strictly local quantum measurements. In particular, we demonstrate that a malicious agent with access to only a small subset of nodes can initialize quantum states locally and, through repeated short-time measurements, extract sensitive structural information about the entire network. The intrusion strategy is inspired by extreme learning and quantum reservoir computing and combines short-time quantum evolution with a non-iterative linear readout with trainable weights. These results suggest new strategies for intrusion detection and structural diagnostics in future quantum Internet infrastructures.