Utility of Quantum Kernel Machines in Remote Sensing Applications

We investigate the runtime of quantum kernel estimation in the view of quantum kernel concentration effect. The study is performed for projected quantum kernel family evaluated on hyperspectral remote sensing data. The effect of exponential value concentration leads to the indistinguishability of the kernel matrix entries as the size of the quantum device grows. In order to prevent that, kernel values have to be estimated with a better precision. Increasing precision inevitably connects to an increasing number of circuit runs, which influences the runtime of quantum algorithm. This, in turn, frequently obstructs a possible advantage for quantum machine learning methods. We find that, against popular opinions, the effect of exponential value concentration does not rule out the utility of quantum kernel methods and the severity of the issue depends highly on the data used.