HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification

• URL: http://arxiv.org/abs/2506.11146v1
• Date: Wed, 11 Jun 2025 09:12:20 GMT
• Title: HQFNN: A Compact Quantum-Fuzzy Neural Network for Accurate Image Classification
• Authors: Jianhong Yao, Yangming Guo,
• Abstract summary: Highly Quantized Fuzzy Neural Network (HQFNN) couples quantum signal to lightweight CNN feature extractor.<n>HQFNN consistently surpasses classical, fuzzy enhanced and quantum only baselines.<n> Gate count analysis shows that circuit depth grows sublinearly with input dimension.
• Score: 0.3595507621009123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning vision systems excel at pattern recognition yet falter when inputs are noisy or the model must explain its own confidence. Fuzzy inference, with its graded memberships and rule transparency, offers a remedy, while parameterized quantum circuits can embed features in richly entangled Hilbert spaces with striking parameter efficiency. Bridging these ideas, this study introduces a innovative Highly Quantized Fuzzy Neural Network (HQFNN) that realises the entire fuzzy pipeline inside a shallow quantum circuit and couples the resulting quantum signal to a lightweight CNN feature extractor. Each image feature is first mapped to a single qubit membership state through repeated angle reuploading. Then a compact rule layer refines these amplitudes, and a clustered CNOT defuzzifier collapses them into one crisp value that is fused with classical features before classification. Evaluated on standard image benchmarks, HQFNN consistently surpasses classical, fuzzy enhanced and quantum only baselines while using several orders of magnitude fewer trainable weights, and its accuracy degrades only marginally under simulated depolarizing and amplitude damping noise, evidence of intrinsic robustness. Gate count analysis further shows that circuit depth grows sublinearly with input dimension, confirming the model's practicality for larger images. These results position the model as a compact, interpretable and noise tolerant alternative to conventional vision backbones and provide a template for future quantum native fuzzy learning frameworks.

Related papers

• Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence [63.39968536637762]
  We introduce Quantum LEGO Learning, a learning framework that treats classical and quantum components as reusable, composable learning blocks.<n>Within this framework, a pre-trained classical neural network serves as a frozen feature block, while a VQC acts as a trainable adaptive module.<n>We develop a block-wise generalization theory that decomposes learning error into approximation and estimation components.
  arXiv  Detail & Related papers  (2026-01-29T14:29:21Z)
• Quantum-Enhanced Neural Contextual Bandit Algorithms [50.880384999888044]
  This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm.<n>QNTK-UCB is a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T09:58:14Z)
• Dual-Qubit Hierarchical Fuzzy Neural Network for Image Classification: Enabling Relational Learning via Quantum Entanglement [27.317371900372127]
  This paper proposes a dual-qubit hierarchical fuzzy neural network (DQ-HFNN)<n>It encodes feature pairs onto a pair of entangled qubits, which extends the single-feature fuzzy model to a joint fuzzy representation.<n> Experiments under noisy conditions suggest that it is robust against noise and has the potential to be implemented on noisy intermediate-scale quantum devices.
  arXiv  Detail & Related papers  (2025-12-15T12:35:53Z)
• Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis [42.138439537056954]
  Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction.<n>We present Quantum Neural Radiance Fields (Q-NeRF), the first hybrid quantum-classical framework for neural radiance field rendering.
  arXiv  Detail & Related papers  (2025-12-14T13:24:11Z)
• Hybrid Quantum-Classical Learning for Multiclass Image Classification [0.15749416770494704]
  We propose a hybrid quantum-classical architecture that couples a modified QCNN with fully connected classical layers.<n>The method outperforms comparable lightweight models on MNIST, Fashion-MNIST and OrganAMNIST.<n>These results indicate that reusing discarded qubit information is a promising approach for future hybrid quantum-classical models.
  arXiv  Detail & Related papers  (2025-08-25T16:12:18Z)
• Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware [0.0]
  We implement a quantum generalization of a network on trapped-ion and IBM superconducting quantum computers.<n>The network feedforward involves qubit rotations whose angles depend on the results of measurements in the previous layer.<n>We benchmark physical noise by inserting additional single-qubit and two-qubit gate pairs into the neural network circuits.
  arXiv  Detail & Related papers  (2025-07-28T18:00:03Z)
• Unitary Scrambling and Collapse: A Quantum Diffusion Framework for Generative Modeling [5.258882634977828]
  We propose QSC-Diffusion, the first fully quantum diffusion-based framework for image generation.<n>We employ parameterized quantum circuits with measurement-induced collapse for reverse denoising.<n>Remarkably, QSC-Diffusion achieves competitive FID scores across multiple datasets.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• 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)
• QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations [1.874615333573157]
  This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations.<n>The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models.<n>Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.
  arXiv  Detail & Related papers  (2025-04-26T23:40:33Z)
• Quantum Latent Diffusion Models [65.16624577812436]
  We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models.<n>This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space.<n>The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
  arXiv  Detail & Related papers  (2025-01-19T21:24:02Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Towards Efficient Quantum Hybrid Diffusion Models [68.43405413443175]
  We propose a new methodology to design quantum hybrid diffusion models. We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
  arXiv  Detail & Related papers  (2024-02-25T16:57:51Z)
• Frequency Disentangled Features in Neural Image Compression [13.016298207860974]
  A neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on-heavy spatially autoregressive entropy models.
  arXiv  Detail & Related papers  (2023-08-04T14:55:44Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• 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)
• Image Classification by Throwing Quantum Kitchen Sinks at Tensor Networks [0.0]
  We propose a new circuit ansatz for quantum machine learning. We run numerical experiments to empirically evaluate the performance of the new ansatz on image classification. The addition of feature optimization greatly boosts performance, leading to state-of-the-art quantum circuits for image classification.
  arXiv  Detail & Related papers  (2022-08-29T21:38:22Z)
• Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers [55.90468016961356]
  We propose an efficient token mixer that learns to mix in the Fourier domain. AFNO is based on a principled foundation of operator learning. It can handle a sequence size of 65k and outperforms other efficient self-attention mechanisms.
  arXiv  Detail & Related papers  (2021-11-24T05:44:31Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• Quantum Self-Supervised Learning [22.953284192004034]
  We propose a hybrid quantum-classical neural network architecture for contrastive self-supervised learning. We apply our best quantum model to classify unseen images on the ibmq_paris quantum computer.
  arXiv  Detail & Related papers  (2021-03-26T18:00:00Z)

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.