Quantum Multi-view Kernel Learning with Local Information

• URL: http://arxiv.org/abs/2505.16484v1
• Date: Thu, 22 May 2025 10:11:49 GMT
• Title: Quantum Multi-view Kernel Learning with Local Information
• Authors: Jing Li, Yanqi Song, Sujuan Qin, Fei Gao,
• Abstract summary: Kernel methods serve as powerful tools to capture nonlinear patterns behind data in machine learning.<n>We propose quantum multi-view kernel learning with local information, called L-QMVKL.<n>Our work holds promise for advancing the theoretical and practical understanding of quantum kernel methods.
• Score: 4.65325662979106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Kernel methods serve as powerful tools to capture nonlinear patterns behind data in machine learning. The quantum kernel, integrating kernel theory with quantum computing, has attracted widespread attention. However, existing studies encounter performance bottlenecks when processing complex data with localized structural patterns, stemming from the limitation in single-view feature representation and the exclusive reliance on global data structure. In this paper, we propose quantum multi-view kernel learning with local information, called L-QMVKL. Specifically, based on the multi-kernel learning, a representative method for multi-view data processing, we construct the quantum multi-kernel that combines view-specific quantum kernels to effectively fuse cross-view information. Further leveraging local information to capture intrinsic structural information, we design a sequential training strategy for the quantum circuit parameters and weight coefficients with the use of the hybrid global-local kernel alignment. We evaluate the effectiveness of L-QMVKL through comprehensive numerical simulations on the Mfeat dataset, demonstrating significant accuracy improvements achieved through leveraging multi-view methodology and local information. Meanwhile, the results show that L-QMVKL exhibits a higher accuracy than its classical counterpart. Our work holds promise for advancing the theoretical and practical understanding of quantum kernel methods.

Related papers

• Benchmarking Quantum Kernels Across Diverse and Complex Data [2.118455551237009]
  We develop a variational quantum kernel framework for complex classification tasks.<n>We conduct a benchmark on eight challenging, real world and high-dimensional datasets.<n>This work demonstrates that properly designed quantum kernels can function as versatile, high-performance tools.
  arXiv  Detail & Related papers  (2025-11-13T22:20:20Z)
• A Resource Efficient Quantum Kernel [1.7249361224827533]
  We introduce a quantum kernel designed to handle high-dimensional data with a significantly reduced number of qubits and entangling operations.<n>Our approach preserves essential data characteristics while promoting computational efficiency.<n>Our findings herald a promising avenue for the practical implementation of quantum machine learning algorithms on near future quantum computing platforms.
  arXiv  Detail & Related papers  (2025-07-04T16:12:57Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Benign Overfitting with Quantum Kernels [5.499796332553708]
  Quantum kernels quantify similarity between data points by measuring the inner product between quantum states.<n>We propose a novel strategy for constructing quantum kernels that achieve good generalization performance.
  arXiv  Detail & Related papers  (2025-03-21T10:30:42Z)
• Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement [0.0]
  We propose a novel architecture, which incorporates pointwise convolution within a quantum neural network framework.<n>By using quantum circuits, we map data to a higher-dimensional space, capturing more complex feature relationships.<n>In experiments, we applied the quantum pointwise convolution layer to classification tasks on the FashionMNIST and CIFAR10 datasets.
  arXiv  Detail & Related papers  (2024-12-02T08:03:59Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• A Unified Framework for Trace-induced Quantum Kernels [0.0]
  Quantum kernel methods are promising candidates for achieving a practical quantum advantage for certain machine learning tasks. In this work we combine all trace-induced quantum kernels into a common framework. We show numerically that models based on local projected kernels can achieve comparable performance to the global fidelity quantum kernel.
  arXiv  Detail & Related papers  (2023-11-22T17:50:00Z)
• Coreset selection can accelerate quantum machine learning models with provable generalization [6.733416056422756]
  Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning. We present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels.
  arXiv  Detail & Related papers  (2023-09-19T08:59:46Z)
• Higher-order topological kernels via quantum computation [68.8204255655161]
  Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data. We propose a quantum approach to defining Betti kernels, which is based on constructing Betti curves with increasing order.
  arXiv  Detail & Related papers  (2023-07-14T14:48:52Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Tracing Information Flow from Open Quantum Systems [52.77024349608834]
  We use photons in a waveguide array to implement a quantum simulation of the coupling of a qubit with a low-dimensional discrete environment. Using the trace distance between quantum states as a measure of information, we analyze different types of information transfer.
  arXiv  Detail & Related papers  (2021-03-22T16:38:31Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.