  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.
