Designing Robust Quantum Neural Networks: Exploring Expressibility, Entanglement, and Control Rotation Gate Selection for Enhanced Quantum Models

• URL: http://arxiv.org/abs/2411.11870v1
• Date: Sun, 03 Nov 2024 21:18:07 GMT
• Title: Designing Robust Quantum Neural Networks: Exploring Expressibility, Entanglement, and Control Rotation Gate Selection for Enhanced Quantum Models
• Authors: Walid El Maouaki, Alberto Marchisio, Taoufik Said, Muhammad Shafique, Mohamed Bennai,
• Abstract summary: This study investigates the robustness of Quanvolutional Neural Networks (QuNNs) in comparison to their classical counterparts. We develop a novel methodology that utilizes three quantum circuit metrics: expressibility, entanglement capability, and controlled rotation gate selection. Our results demonstrate that QuNNs exhibit up to 60% greater robustness on the MNIST dataset and 40% on the Fashion-MNIST dataset compared to CNNs.
• Score: 3.9554540293311864
• License:
• Abstract: In this study, we investigated the robustness of Quanvolutional Neural Networks (QuNNs) in comparison to their classical counterparts, Convolutional Neural Networks (CNNs), against two adversarial attacks: FGSM and PGD, for the image classification task on both MNIST and FMNIST datasets. To enhance the robustness of QuNNs, we developed a novel methodology that utilizes three quantum circuit metrics: expressibility, entanglement capability, and controlled rotation gate selection. Our analysis shows that these metrics significantly influence data representation within the Hilbert space, thereby directly affecting QuNN robustness. We rigorously established that circuits with higher expressibility and lower entanglement capability generally exhibit enhanced robustness under adversarial conditions, particularly at low-spectrum perturbation strengths where most attacks occur. Furthermore, our findings challenge the prevailing assumption that expressibility alone dictates circuit robustness; instead, we demonstrate that the inclusion of controlled rotation gates around the Z-axis generally enhances the resilience of QuNNs. Our results demonstrate that QuNNs exhibit up to 60% greater robustness on the MNIST dataset and 40% on the Fashion-MNIST dataset compared to CNNs. Collectively, our work elucidates the relationship between quantum circuit metrics and robust data feature extraction, advancing the field by improving the adversarial robustness of QuNNs.

Related papers

• Physical Rule-Guided Convolutional Neural Network [0.0]
  Physics-Guided Neural Networks (PGNNs) have emerged to address limitations by integrating scientific principles and real-world knowledge. This paper proposes a novel Physics-Guided CNN (PGCNN) architecture that incorporates dynamic, trainable, and automated LLM-generated, widely recognized rules integrated into the model as custom layers. The PGCNN is evaluated on multiple datasets, demonstrating superior performance compared to a baseline CNN model.
  arXiv  Detail & Related papers  (2024-09-03T17:32:35Z)
• RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding [17.342181435229573]
  Spiking Neural Networks (SNNs) have received widespread attention due to their unique neuronal dynamics and low-power nature. Previous research empirically shows that SNNs with Poisson coding are more robust than Artificial Neural Networks (ANNs) on small-scale datasets. This work theoretically demonstrates that SNN's inherent adversarial robustness stems from its Poisson coding.
  arXiv  Detail & Related papers  (2024-07-29T15:26:15Z)
• RobQuNNs: A Methodology for Robust Quanvolutional Neural Networks against Adversarial Attacks [3.9554540293311864]
  Quanvolutional Neural Networks (QuNNs) integrate quantum and classical layers. This study introduces RobQuNN, a new methodology to enhance the robustness of QuNNs against adversarial attacks. The findings reveal that QuNNs exhibit up to 60% higher robustness compared to classical networks for the MNIST dataset.
  arXiv  Detail & Related papers  (2024-07-04T12:13:52Z)
• Parallel Proportional Fusion of Spiking Quantum Neural Network for Optimizing Image Classification [10.069224006497162]
  We introduce a novel architecture termed Parallel Proportional Fusion of Quantum and Spiking Neural Networks (PPF-QSNN) The proposed PPF-QSNN outperforms both the existing spiking neural network and the serial quantum neural network across metrics such as accuracy, loss, and robustness. This study lays the groundwork for the advancement and application of quantum advantage in artificial intelligent computations.
  arXiv  Detail & Related papers  (2024-04-01T10:35:35Z)
• AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks [3.9554540293311864]
  This study aims to rigorously assess the influence of quantum circuit architecture on the resilience of QuNN models. Our results show that, compared to classical convolutional networks, QuNNs achieve up to 60% higher robustness for the MNIST and 40% for FMNIST datasets.
  arXiv  Detail & Related papers  (2024-03-07T12:30:40Z)
• On the Intrinsic Structures of Spiking Neural Networks [66.57589494713515]
  Recent years have emerged a surge of interest in SNNs owing to their remarkable potential to handle time-dependent and event-driven data. There has been a dearth of comprehensive studies examining the impact of intrinsic structures within spiking computations. This work delves deep into the intrinsic structures of SNNs, by elucidating their influence on the expressivity of SNNs.
  arXiv  Detail & Related papers  (2022-06-21T09:42:30Z)
• Knowledge Enhanced Neural Networks for relational domains [83.9217787335878]
  We focus on a specific method, KENN, a Neural-Symbolic architecture that injects prior logical knowledge into a neural network. In this paper, we propose an extension of KENN for relational data.
  arXiv  Detail & Related papers  (2022-05-31T13:00:34Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• On the benefits of robust models in modulation recognition [53.391095789289736]
  Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations. We propose a novel framework to test the robustness of current state-of-the-art models.
  arXiv  Detail & Related papers  (2021-03-27T19:58:06Z)
• Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective [91.5105021619887]
  Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) BN is observed to increase the model accuracy while at the cost of adversarial robustness. It remains unclear whether BN mainly favors learning robust features (RFs) or non-robust features (NRFs)
  arXiv  Detail & Related papers  (2020-10-07T10:24:33Z)

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.