  Quantum machine learning (QML) is a fast-growing discipline within quantum
computing. One popular QML algorithm, quantum kernel estimation, uses quantum
circuits to estimate a similarity measure (kernel) between two classical
feature vectors. Given a set of such circuits, we give a heuristic, predictive
model for the total circuit execution time required, based on a
recently-introduced measure of the speed of quantum computers. In doing so, we
also introduce the notion of an "effective number of quantum volume layers of a
circuit", which may be of independent interest. We validate the performance of
this model using synthetic and real data by comparing the model's predictions
to empirical runtime data collected from IBM Quantum computers through the use
of the Qiskit Runtime service. At current speeds of today's quantum computers,
our model predicts data sets consisting of on the order of hundreds of feature
vectors can be processed in order a few hours. For a large-data workflow, our
model's predictions for runtime imply further improvements in the speed of
circuit execution -- as well as the algorithm itself -- are necessary.
