Fast Fingerprinting of Cloud-based NISQ Quantum Computers

TL;DR

This paper presents a rapid and reliable method for fingerprinting cloud-based superconducting quantum computers using their unique qubit frequency characteristics, enabling differentiation and identification of devices.

Contribution

The work introduces a novel, fast fingerprinting technique based on qubit frequencies that distinguishes quantum computers with high accuracy and minimal operational overhead.

Findings

Fingerprinting method is fast and requires minimal executions.

High inter-device separation ensures clear differentiation.

Stable intra-device fingerprints over time.

Abstract

Cloud-based quantum computers have become a reality with a number of companies allowing for cloud-based access to their machines with tens to more than 100 qubits. With easy access to quantum computers, quantum information processing will potentially revolutionize computation, and superconducting transmon-based quantum computers are among some of the more promising devices available. Cloud service providers today host a variety of these and other prototype quantum computers with highly diverse device properties, sizes, and performances. The variation that exists in today's quantum computers, even among those of the same underlying hardware, motivate the study of how one device can be clearly differentiated and identified from the next. As a case study, this work focuses on the properties of 25 IBM superconducting, fixed-frequency transmon-based quantum computers that range in age from a few months to approximately 2.5 years. Through the analysis of current and historical quantum computer calibration data, this work uncovers key features within the machines that can serve as basis for unique hardware fingerprint of each quantum computer. This work demonstrates a new and fast method to reliably fingerprint cloud-based quantum computers based on unique frequency characteristics of transmon qubits. Both enrollment and recall operations are very fast as fingerprint data can be generated with minimal executions on the quantum machine. The qubit frequency-based fingerprints also have excellent inter-device separation and intra-device stability.