HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks

• URL: http://arxiv.org/abs/2605.14523v1
• Date: Thu, 14 May 2026 08:07:58 GMT
• Title: HQTN-SER: Speech Emotion Recognition with Hybrid Quantum Tensor Networks
• Abstract summary: Speech emotion recognition remains fragile in real-world conditions.<n>Quantum machine learning offers an alternative way to introduce nonlinear correlation modeling with compact modules.<n>This paper presents HQTN-SER, a hybrid quantum-classical framework that investigates how quantum tensor network connectivity can support SER under small-qubit settings.
• Score: 2.417944170423441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech emotion recognition (SER) remains fragile in real-world conditions because emotional cues are subtle, speaker-dependent, and easily confounded by recording variability, while high-performing deep models typically rely on large and carefully curated training sets. Quantum machine learning offers an alternative way to introduce nonlinear correlation modeling with compact modules, yet existing quantum SER studies remain limited and the impact of circuit structure is not well understood. This paper presents HQTN-SER, a hybrid quantum-classical framework that investigates how quantum tensor network connectivity can support SER under small-qubit settings. HQTN-SER introduces (i) an MPS-inspired quantum tensor network module that enforces structured interactions to model correlations in speech representations with a small number of trainable parameters, and (ii) a fusion strategy that combines quantum measurement features with a learned classical latent embedding for end-to-end emotion classification. We evaluate HQTN-SER on three public benchmarks (RAVDESS, SAVEE, and MDER) under a unified preprocessing and training protocol. The proposed model achieves consistent performance across datasets, RAVDESS = 80.12%, SAVEE = 78.26% and MDER = 73.51% accuracy, with stable convergence and low qubit counts, showing that tensor network structure can be an effective and hardware-aware design choice for quantum-assisted SER. The results provide a reproducible baseline and clarify when structured quantum modules can add value to affective computing today.

Related papers

• Quantum Incremental Learning with Mixed State Prototypes [53.319426778720434]
  We introduce a novel quantum incremental learning framework based on trainable mixed-state prototypes.<n>Our model achieves high-dimensional feature concentration using a minimal number of qubits.
  arXiv  Detail & Related papers  (2026-08-11T04:22:42Z)
• 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)
• Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware [0.764671395172401]
  We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models.<n>Our architecture combines hardware-optimized parametric quantum circuits with a lightweight classical projection layer.<n>Experiments on IBM Quantum processors reveal the critical trade-offs between circuit depth and trainability.
  arXiv  Detail & Related papers  (2025-12-14T14:22:44Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning [0.0]
  Quantum Graph Attention Network (QGAT) is a hybrid graph neural network that integrates variational quantum circuits into the attention mechanism.<n>We show QGAT's effectiveness in capturing complex structural dependencies and improved generalization in inductive scenarios.<n>Experiments confirm that quantum embedding enhances robustness against feature and structural noise, suggesting advantages in handling real-world noisy data.
  arXiv  Detail & Related papers  (2025-08-25T03:25:48Z)
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing [50.95799256262098]
  We introduceHyper-VQC, a novel tensor-train (TT)-guided hypernetwork framework for quantum machine learning.<n>Our framework delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.<n>These results positionHyper-VQC as a scalable and noise-resilient framework for advancing practical quantum machine learning on near-term devices.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• 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)
• Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition [37.98283871637917]
  We investigate the use of quantum circuits (PQCs) for speech emotion recognition (SER)<n>We propose a hybrid quantum classical architecture that integrates PQCs into a conventional convolutional neural network (CNN)<n>Our model achieves improved classification performance relative to a purely classical CNN baseline, with over 50% reduction in trainable parameters.
  arXiv  Detail & Related papers  (2025-01-21T11:23:38Z)
• Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz [0.8192907805418583]
  Quantum-Train (QT) is a hybrid quantum-classical machine learning framework. It maps quantum state measurements to classical neural network weights. Traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. We introduce a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes.
  arXiv  Detail & Related papers  (2024-09-11T03:51:34Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• 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)
• An end-to-end trainable hybrid classical-quantum classifier [0.0]
  We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework.
  arXiv  Detail & Related papers  (2021-02-04T05:19:54Z)

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.