Author: Marco Russo, Edoardo Giusto, Bartolomeo Montrucchio

Original Paper

In this paper, the authors propose a gate-based approach to quantum kernel estimation (QKE) for supervised classification using neutral atom quantum computers. QKE is a technique that leverages the power of quantum computing to estimate a kernel function that is difficult to compute classically. The estimated kernel is then used by a classical computer to train a support vector machine (SVM) for classification tasks. The authors focus on neutral atom quantum computers because they allow for more freedom in arranging the atoms, which is essential for implementing the necessary gates for QKE. They present a general method for deriving 1-qubit and 2-qubit gates from laser pulses, which are then used to construct a parameterized sequence for feature mapping on 3 qubits. They show that this approach can be extended to N qubits, taking advantage of the more flexible arrangement of atoms in neutral atom devices. The experimental setup involves simulating the Pasqal Chadoq2 device, which allows for planar arrangement of atoms. The authors generate a dataset of 40 training samples and 20 test samples with 3 features and a separation gap of 0.1. They use the Qiskit library to implement the feature mapping circuit and generate the sequences of pulses for QKE. The training and testing of the SVM are performed on a classical computer using the estimated kernel matrices. The results show that the proposed approach achieves a high accuracy of 75% on the test set, despite the small size of the dataset and the low separation. The authors compare the performance to a classical SVM with a radial basis function kernel and find that the quantum approach outperforms the classical approach. The authors discuss the advantages of using neutral atom quantum computers for QKE. The arbitrary arrangement of atoms allows for more direct connections between qubits, reducing the depth of the circuit and reducing the impact of decoherence. They also highlight the exponential computational advantage of quantum feature kernels over classical kernel computation methods for high-dimensional feature spaces. Overall, the paper presents a gate-based approach to QKE using neutral atom quantum computers. The experimental results demonstrate the potential of this approach for supervised classification tasks and highlight the advantages of neutral atom devices for implementing QKE circuits. The paper provides a foundation for future research in the field of quantum machine learning and quantum computing.