Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ Quantum Computers

TL;DR

This paper introduces a method for fingerprinting shared NISQ quantum computers using idle tomography to detect device-specific errors, achieving high prediction accuracy and highlighting security concerns in cloud-based quantum computing.

Contribution

It presents a novel idle tomography-based fingerprinting technique that identifies quantum devices with high accuracy, revealing new security vulnerabilities in shared quantum cloud services.

Findings

Prediction accuracy of 99.1% for device-specific fingerprinting.

Prediction accuracy of 95.3% for locality-specific fingerprinting.

Demonstrates a new security threat to shared quantum cloud infrastructure.

Abstract

Fingerprinting of quantum computer devices is a new threat that poses a challenge to shared, cloud-based quantum computers. Fingerprinting can allow adversaries to map quantum computer infrastructures, uniquely identify cloud-based devices which otherwise have no public identifiers, and it can assist other adversarial attacks. This work shows idle tomography-based fingerprinting method based on crosstalk-induced errors in NISQ quantum computers. The device- and locality-specific fingerprinting results show prediction accuracy values of $99.1\%$ and $95.3\%$, respectively.