Merge branch 'main' of https://github.com/apache/mahout

website/_papers/Quantum-Kernel-Estimation-With-Neutral-Atoms-For-Supervised-Classification.md

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+---
+layout: post
+title: "Summary of 'Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach'"
+date: 2024-03-06
+---
+
+Author: Marco Russo, Edoardo Giusto, Bartolomeo Montrucchio
+
+[Original Paper](https://arxiv.org/abs/2307.15840)
+
+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.

website/_papers/Quantum-machine-learning-beyond-kernel-methods.md

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+---
+layout: post
+title: "Summary of 'Quantum machine learning beyond kernel methods'"
+date: 2024-03-06
+---
+
+Author: Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko
+
+Journal: Nature Communications
+
+[Original Paper](https://www.nature.com/articles/s41467-023-36159-y)
+
+This article presents a study on quantum machine learning models and their 
+comparison to classical models. The authors propose a framework that captures 
+all standard models based on parametrized quantum circuits, focusing on linear 
+quantum models. They analyze the resource requirements and learning performance 
+guarantees of these models, particularly comparing explicit and implicit models.
+They show that implicit models can achieve a lower training loss but may suffer 
+from poor generalization performance. They also show that data re-uploading 
+models, a type of explicit model, can be more general than both explicit and 
+implicit models. The authors further investigate the advantages of explicit 
+models by testing their performance on a learning task involving 
+quantum-generated data. They find that explicit models can outperform both 
+implicit models and classical models on this task, highlighting the potential 
+learning advantage of explicit quantum models. The study provides insights into 
+the capabilities and limitations of different quantum machine learning models, 
+and it contributes to understanding the possible advantages of quantum models in
+practical applications.