The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment

• URL: http://arxiv.org/abs/2601.04732v1
• Date: Thu, 08 Jan 2026 08:54:44 GMT
• Title: The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
• Authors: Dominik Freinberger, Philipp Moser,
• Abstract summary: Quantum machine learning has emerged as a promising application domain for near-term quantum hardware.<n>We aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study. We systematically assess common hybrid models on medical signal data as well as planar and volumetric images, examining the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. We find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts, however, in most cases, performance metrics deteriorate under the influence of quantum components. Our multi-modal analysis provides realistic insights into the contributions of quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.

Related papers

• Practical Quantum-Classical Feature Fusion for complex data Classification [0.0]
  Hybrid quantum and classical learning aims to couple quantum feature maps with the robustness of classical neural networks.<n>We present a multimodal formulation of hybrid learning and propose a cross attention mid fusion architecture.<n>We evaluate on Wine, Breast Cancer, Forest CoverType, FashionMNIST, and SteelPlatesFaults, comparing a quantum only model, a classical baseline, residual hybrid models, and the proposed mid fusion model.
  arXiv  Detail & Related papers  (2025-12-22T09:16:08Z)
• Enhanced image classification via hybridizing quantum dynamics with classical neural networks [0.0]
  We present a hybrid protocol which combines classical neural networks with non-equilibrium dynamics of a quantum many-body system for image classification.<n>This architecture leverages classical neural networks to efficiently process high-dimensional data and encode it effectively on a quantum many-body system.
  arXiv  Detail & Related papers  (2025-07-18T00:15:14Z)
• 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)
• 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)
• LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder [7.945302052915863]
  A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data. We propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder.
  arXiv  Detail & Related papers  (2024-09-22T23:18:06Z)
• 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)
• 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)
• Peptide Binding Classification on Quantum Computers [3.9540968630765643]
  We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology. We perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale. This work constitutes the first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins.
  arXiv  Detail & Related papers  (2023-11-27T10:32:31Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• 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)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)

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.