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

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

URL: http://arxiv.org/abs/2110.08689v1
Date: Sun, 17 Oct 2021 00:45:31 GMT
Title: Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Authors: Jun Qi, Javier Tejedor
Abstract summary: 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.
Score: 13.485144642413907
License: http://creativecommons.org/licenses/by/4.0/
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.