Hybrid quantum-classical neural network for sentiment analysis

• URL: http://arxiv.org/abs/2607.01943v1
• Date: Thu, 02 Jul 2026 09:34:27 GMT
• Title: Hybrid quantum-classical neural network for sentiment analysis
• Abstract summary: We investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis.<n>Our results show that hybrid models can achieve accuracy comparable to the classical baseline.
• Score: 4.351505522514462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.

Related papers

• Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics [54.3410887955176]
  Crime pattern analysis is critical for law enforcement and predictive policing.<n>The surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets.<n>This study presents a quantum-classical comparison framework for crime analytics.
  arXiv  Detail & Related papers  (2026-04-08T06:05:25Z)
• 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 AI for Cybersecurity: A hybrid Quantum-Classical models for attack path analysis [1.2676356746752895]
  This study investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection.<n>We show that quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce.<n>Results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks.
  arXiv  Detail & Related papers  (2026-01-05T16:11:39Z)
• Hybrid Quantum-Classical Generative Adversarial Networks with Transfer Learning [0.0]
  Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images.<n>In this paper, we investigate hybrid quantum-classical GAN architectures supplemented by transfer learning.<n>Our findings indicate that fully hybrid models, which incorporate VQCs in both the generator and the discriminator, consistently produce images of higher visual quality.
  arXiv  Detail & Related papers  (2025-07-13T16:46:56Z)
• 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)
• Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection [0.1398098625978622]
  We present a novel hybrid quantum-classical neural network architecture for fraud detection.<n>By leveraging quantum phenomena such as superposition and entanglement, our model enhances the feature representation of sequential transaction data.<n>Results demonstrate competitive improvements in accuracy, precision, recall, and F1 score relative to a conventional LSTM baseline.
  arXiv  Detail & Related papers  (2025-04-30T19:09:12Z)
• Lean classical-quantum hybrid neural network model for image classification [12.353900068459446]
  We introduce a Lean Classical-Quantum Hybrid Neural Network (LCQHNN), which achieves efficient classification performance with only four layers of variational circuits.<n>Our experiments demonstrate that LCQHNN achieves 100%, 99.02%, and 85.55% classification accuracy on MNIST, FashionMNIST, and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2024-12-03T00:37:11Z)
• 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)
• Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation [0.0]
  We present a novel framework for transferring knowledge from classical convolutional neural networks (CNNs) to quantum neural networks (QNNs)<n>We conduct extensive experiments using two parameterized quantum circuits (PQCs) with 4 and 8 qubits on MNIST, Fashion MNIST, and CIFAR10 datasets.<n>Our results establish a promising paradigm for bridging classical deep learning and emerging quantum computing, paving the way for more powerful, resource conscious models in quantum machine intelligence.
  arXiv  Detail & Related papers  (2023-11-23T05:06:43Z)
• 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)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks [13.485144642413907]
  This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR) We put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset.
  arXiv  Detail & Related papers  (2021-10-17T00:45:31Z)

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.