Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

• URL: http://arxiv.org/abs/2608.01194v1
• Date: Sun, 02 Aug 2026 12:21:00 GMT
• Title: Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
• Abstract summary: Hybrid quantum neural networks combine classical neural-network components with quantum information processing units.<n>We review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.

Related papers

• Leveraging Quantum Layers in Classical Neural Networks [0.0]
  This thesis explores the integration of quantum layers within classical convolutional neural network architectures.<n> Experiments investigate the performance impact of inserting quantum layers at different stages of the neural network pipeline.
  arXiv  Detail & Related papers  (2025-07-16T15:12:53Z)
• Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications [59.0413662882849]
  Quantum computing is poised to redefine the algorithmic foundations of communication systems.<n>This article outlines the fundamentals of quantum computing in a style familiar to the communications society.<n>We highlight a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers.
  arXiv  Detail & Related papers  (2025-06-25T22:25:47Z)
• A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations [0.0]
  Quantum Generative Adversarial Networks (QGANs) have emerged as a promising direction in quantum machine learning.<n>This survey provides a comprehensive overview of QGAN models, highlighting key advances from theoretical proposals to experimental realizations.
  arXiv  Detail & Related papers  (2025-06-22T11:45:27Z)
• 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)
• Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications [0.0]
  This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models.<n>We present several examples of these quantum machine learning transformations and explore their potential applications.
  arXiv  Detail & Related papers  (2025-03-10T11:00:48Z)
• The Impact of Architecture and Cost Function on Dissipative Quantum Neural Networks [0.016385815610837167]
  We present a novel architecture for dissipative quantum neural networks (DQNNs) in which each building block can implement any quantum channel.<n>We derive a versatile one-to-one parametrization of isometries, allowing for an efficient implementation of the proposed structure.
  arXiv  Detail & Related papers  (2025-02-13T17:38:48Z)
• Entanglement-Assisted Quantum Networks: Mechanics, Enabling Technologies, Challenges, and Research Directions [66.27337498864556]
  This paper presents a comprehensive survey of entanglement-assisted quantum networks. It provides a detailed overview of the network structure, working principles, and development stages. It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
  arXiv  Detail & Related papers  (2023-07-24T02:48:22Z)
• 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)
• An Invitation to Distributed Quantum Neural Networks [0.0]
  We review the current state of the art in distributed quantum neural networks. We find that the distribution of quantum datasets shares more similarities with its classical counterpart than does the distribution of quantum models.
  arXiv  Detail & Related papers  (2022-11-14T00:27:01Z)
• Physics-Informed Quantum Communication Networks: A Vision Towards the Quantum Internet [79.8886946157912]
  We present a novel analysis of the performance of quantum communication networks (QCNs) in a physics-informed manner. The need of the physics-informed approach is then assessed and its fundamental role in designing practical QCNs is analyzed. We identify novel physics-informed performance metrics and controls that enable QCNs to leverage the state-of-the-art advancements in quantum technologies.
  arXiv  Detail & Related papers  (2022-04-20T05:32:16Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)
• 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.