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Quantum machine learning (QML) is a fast-growing discipline within quantumcomputing. One popular QML algorithm, quantum kernel estimation, uses quantumcircuits to estimate a similarity measure (kernel) between two classicalfeature vectors. Given a set of such circuits, we give a heuristic, predictivemodel for the total circuit execution time required, based on arecently-introduced measure of the speed of quantum computers. In doing so, wealso introduce the notion of an "effective number of quantum volume layers of acircuit", which may be of independent interest. We validate the performance ofthis model using synthetic and real data by comparing the model's predictionsto empirical runtime data collected from IBM Quantum computers through the useof 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 featurevectors can be processed in order a few hours. For a large-data workflow, ourmodel's predictions for runtime imply further improvements in the speed ofcircuit execution -- as well as the algorithm itself -- are necessary.

A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes