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Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantumcomputer to estimate a kernel function that is classically difficult tocalculate, which is then used by a classical computer for training a SupportVector Machine (SVM). Given the high number of 2-local operators necessary forrealizing a feature mapping hard to simulate classically, a high qubitconnectivity is needed, which is not currently possible on superconductingdevices. For this reason, neutral atom quantum computers can be used, sincethey allow to arrange the atoms with more freedom. Examples ofneutral-atom-based QKE can be found in the literature, but they are focused ongraph learning and use the analogue approach. In this paper, a general methodbased on the gate model is presented. After deriving 1-qubit and 2-qubit gatesstarting from laser pulses, a parameterized sequence for feature mapping on 3qubits is realized. This sequence is then used to empirically compute thekernel matrix starting from a dataset, which is finally used to train the SVM.It is also shown that this process can be generalized up to N qubits takingadvantage of the more flexible arrangement of atoms that this technologyallows. The accuracy is shown to be high despite the small dataset and the lowseparation. This is the first paper that not only proposes an algorithm forexplicitly deriving a universal set of gates but also presents a method ofestimating quantum kernels on neutral atom devices for general problems usingthe gate model.

Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach