Coreset selection can accelerate quantum machine learning models with
provable generalization
- URL: http://arxiv.org/abs/2309.10441v2
- Date: Tue, 12 Dec 2023 23:24:56 GMT
- Title: Coreset selection can accelerate quantum machine learning models with
provable generalization
- Authors: Yiming Huang, Huiyuan Wang, Yuxuan Du, Xiao Yuan
- Abstract summary: 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.
- Score: 6.733416056422756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum neural networks (QNNs) and quantum kernels stand as prominent figures
in the realm of quantum machine learning, poised to leverage the nascent
capabilities of near-term quantum computers to surmount classical machine
learning challenges. Nonetheless, the training efficiency challenge poses a
limitation on both QNNs and quantum kernels, curbing their efficacy when
applied to extensive datasets. To confront this concern, we present a unified
approach: coreset selection, aimed at expediting the training of QNNs and
quantum kernels by distilling a judicious subset from the original training
dataset. Furthermore, we analyze the generalization error bounds of QNNs and
quantum kernels when trained on such coresets, unveiling the comparable
performance with those training on the complete original dataset. Through
systematic numerical simulations, we illuminate the potential of coreset
selection in expediting tasks encompassing synthetic data classification,
identification of quantum correlations, and quantum compiling. Our work offers
a useful way to improve diverse quantum machine learning models with a
theoretical guarantee while reducing the training cost.
Related papers
- Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective [7.7063925534143705]
We introduce the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with machine learning algorithms.
QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model.
arXiv Detail & Related papers (2024-05-18T14:35:57Z) - 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) - Quantum Generative Adversarial Networks: Bridging Classical and Quantum
Realms [0.6827423171182153]
We explore the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs)
Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes.
This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems.
arXiv Detail & Related papers (2023-12-15T16:51:36Z) - Expressibility-induced Concentration of Quantum Neural Tangent Kernels [4.561685127984694]
We study the connections between the trainability and expressibility of quantum tangent kernel models.
For global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero.
Our discoveries unveil a pivotal characteristic of quantum neural tangent kernels, offering valuable insights for the design of wide quantum variational circuit models.
arXiv Detail & Related papers (2023-11-08T19:00:01Z) - The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for
Deep Quantum Machine Learning [52.77024349608834]
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.
Key issue is how to address the inherent non-linearity of classical deep learning.
We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
arXiv Detail & Related papers (2022-12-22T16:06:24Z) - 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) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Comparing concepts of quantum and classical neural network models for
image classification task [0.456877715768796]
This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network.
Although its simulation is time-consuming, the quantum network, although its simulation is time-consuming, overcomes the classical network.
arXiv Detail & Related papers (2021-08-19T18:49:30Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - Entanglement Classification via Neural Network Quantum States [58.720142291102135]
In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states.
We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
arXiv Detail & Related papers (2019-12-31T07:40:23Z)
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.