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arXiv:2110.08689

Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks

Qi, Jun, Javier Tejedor

Published Oct 17, 2021• 7 citations
arXivHTMLPDF

Abstract

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). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, 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. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.

DOI
10.48550/arxiv.2110.08689

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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). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, 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. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
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). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, 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. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
Speech recognition is a hard problem that aims to use machines to understand human speech.This work focuses on the smaller subsection of the speech recognition called, spoken command recognition (SCR).SCR aims to recognize one-word commands instead of full sentences.The area of SCR is well researched and there have been breakthroughs especially when convolutional neural networks (CNN) were introduced.Nowadays SCR is widely used, for example Apple Siri and Amazon Alexa.Recent breakthroughs on the field of quantum computing have made it possible to experiment SCR task with quantum convolutional neural networks (QCNN) and to test if QCNN provides some benefit over classic CNN.This work focuses more on the study by Yang et al. (2021).Yang et al. (2021) proposed a QCNN method for SCR and reported over 95 % accuracy on 10 command dataset.This work extends their study to 35 commands and tests whether or not QCNN performs better with limited amount of training-data or network parameters.According to our experiments, adding more commands reduces model accuracy around 3 %, but QCNN seems to perform better with limited training-data and slightly better with limited parameters.In comparison between CNN and QCNN, CNN seems to perform slightly better than QCNN.
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti.
The development of quantum machine learning demonstrates its quantum advantages over traditional deep learning, which promises to discover new patterns on supervised classification datasets. This work proposes a classical-to-quantum transfer learning system based on the large-scale unsupervised pre-trained model to demonstrate the competitive performance of quantum transfer learning for synthetic speech detection. We use the pre-trained model WavLM-Large to extract feature maps from speech signals, obtain low-dimensional embedding vectors through classical network components, and then jointly fine-tune the pre-trained model and classical network components with a variational quantum circuit (VQC). We evaluate our system on the ASVspoof 2021 DF task, and the experiments using quantum circuit simulations show that quantum transfer learning can improve the performance of the classical transfer learning baseline on the task.
Speech emotion recognition (SER) has gained utmost attention among researchers for its vital role in Human-Computer Interaction, enabling systems to understand and respond to the emotional states of the users. In this work, the concept of transfer learning has been extended to the novel domain of hybrid classical-quantum neural networks for enhancing SER performance. The classical part involves preprocessing raw speech signals to generate three-channel log mel-spectrograms and the pretrained VGG 19 network for extracting useful emotional features. The quantum part is built upon a dressed quantum neural network (QNN), comprising multiple parallel QNN blocks, for accurate emotion classification. Extensive experiments on the publicly accessible TESS dataset reveal that the proposed hybrid transfer learning approach achieves state of the art accuracy of 98.93 %. An ablation study demonstrates the significant performance improvement attributed to the incorporation of QNN blocks. The integration of classical and quantum neural networks provides a robust and efficient solution, leveraging the strengths of both computational paradigms to enhance SER based applications.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.