Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification

• URL: http://arxiv.org/abs/2510.12291v1
• Date: Tue, 14 Oct 2025 08:52:14 GMT
• Title: Hybrid Vision Transformer and Quantum Convolutional Neural Network for Image Classification
• Authors: Mingzhu Wang, Yun Shang,
• Abstract summary: ViT-QCNN-FT is a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network.<n>We show that ansatzes with uniformly distributed entanglement entropy consistently deliver superior non-local feature fusion.<n> substituting the QCNN with classical counterparts of equal parameter count leads to a dramatic 29.36% drop, providing unambiguous evidence of quantum advantage.
• Score: 2.920575300184085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum machine learning (QML) holds promise for computational advantage, yet progress on real-world tasks is hindered by classical preprocessing and noisy devices. We introduce ViT-QCNN-FT, a hybrid framework that integrates a fine-tuned Vision Transformer with a quantum convolutional neural network (QCNN) to compress high-dimensional images into features suited for noisy intermediate-scale quantum (NISQ) devices. By systematically probing entanglement, we show that ansatzes with uniformly distributed entanglement entropy consistently deliver superior non-local feature fusion and state-of-the-art accuracy (99.77% on CIFAR-10). Surprisingly, quantum noise emerges as a double-edged factor: in some cases, it enhances accuracy (+2.71% under amplitude damping). Strikingly, substituting the QCNN with classical counterparts of equal parameter count leads to a dramatic 29.36% drop, providing unambiguous evidence of quantum advantage. Our study establishes a principled pathway for co-designing classical and quantum architectures, pointing toward practical QML capable of tackling complex, high-dimensional learning tasks.

Related papers

• Quantum Visual Fields with Neural Amplitude Encoding [70.86293548779774]
  We introduce a new type of Quantum Implicit Neural Representation (QINR) for 2D image and 3D geometric field learning.<n>QVF encodes classical data into quantum statevectors using neural amplitude encoding grounded in a learnable energy manifold.<n>Our ansatz follows a fully entangled design of learnable parametrised quantum circuits, with quantum (unitary) operations performed in the real Hilbert space.
  arXiv  Detail & Related papers  (2025-08-14T17:59:52Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation [4.358861563008207]
  Quantum neural networks (QNNs) have shown promise both empirically and theoretically.<n> Hardware imperfections and limited access to quantum devices pose practical challenges.<n>We propose an automated solution using differentiable optimization.
  arXiv  Detail & Related papers  (2025-05-13T19:01:08Z)
• Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints [2.3895835682351287]
  Quantum convolutional neural networks (CNNs) hold potential to outperform classical approaches.<n>We introduce an encoding scheme that significantly reduces the input dimensionality.<n>We validate our experiments on IBM's Heron r2 quantum processor, achieving $96.08%$ classification accuracy.
  arXiv  Detail & Related papers  (2025-05-09T11:09:52Z)
• Quantum Adaptive Self-Attention for Quantum Transformer Models [0.0]
  We propose Quantum Adaptive Self-Attention (QASA), a novel hybrid architecture that enhances classical Transformer models with a quantum attention mechanism.<n>QASA replaces dot-product attention with a parameterized quantum circuit (PQC) that adaptively captures inter-token relationships in the quantum Hilbert space.<n> Experiments on synthetic time-series tasks demonstrate that QASA achieves faster convergence and superior generalization compared to both standard Transformers and reduced classical variants.
  arXiv  Detail & Related papers  (2025-04-05T02:52:37Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Variational Quantum-Neural Hybrid Error Mitigation [6.555128824546528]
  Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers. We show how variational quantum-neural hybrid eigensolver (VQNHE) algorithm is inherently noise resilient with a unique QEM capacity.
  arXiv  Detail & Related papers  (2021-12-20T08:07:58Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.