Short Paper: Device- and Locality-Specific Fingerprinting of Shared NISQ Quantum Computers
This addresses a security problem for cloud-based quantum computing users by enabling adversaries to uniquely identify devices, though it is incremental as it builds on existing fingerprinting concepts.
The paper tackled the threat of fingerprinting in shared NISQ quantum computers by proposing an idle tomography-based method using crosstalk-induced errors, achieving prediction accuracies of 99.1% for device-specific and 95.3% for locality-specific fingerprinting.
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.