Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Pattern Recognition and Artificial Intelligence (PRAI)

Quantum Joint Distribution Adaptation on the Universal Quantum Computer

Feiyu Du, Xi He

Published Aug 18, 2023• 0 citations
Publisher

Abstract

Transfer learning (TL) is one of the most important sub-realms in the field of machine learning. The goal is to achieve knowledge transfer from source domain to target domain by utilizing the similarities among data, tasks, or models. Domain adaptation (DA) is actually a type of transductive TL with assumption that both the marginal and the conditional distribution adaptation are different. In this paper, the process of joint distribution adaptation implemented on the universal quantum computer is presented. Compared to the classical joint distribution adaptation, the performance and application scope of the algorithm are promoted by the advantages of the quantum operations. Specifically, the quantum joint distribution adaptation algorithm proposed in our work can be performed exponentially efficient with quantum computing techniques in contrast to the corresponding classical procedure.

DOI
10.1109/prai59366.2023.10332071

Related papers

Quantum kernel subspace alignment for unsupervised domain adaptation

Xi He, Feiyu Du

Domain adaptation (DA), the sub-realm of the transfer learning, attempts to deal with machine learning tasks on an unprocessed data domain with the different, but related labeled source domain. However, the classical DA can not efficiently deal with the cross-domain tasks in quantum mechanical scenarios. In this paper, the quantum kernel subspace alignment algorithm is proposed to achieve the procedure of DA by extracting the non-linear features with the quantum kernel method and aligning the two domains with the unitary evolution. The method presented in our work can be implemented on the universal quantum computer with the quantum basic linear algebra subroutines. Based on the algorithmic complexity analysis, the procedure of the QKSA can be implemented with at least quadratic quantum speedup compared with the classical DA algorithms.

Asia Conference on Algorithms, Computing and Machine Learning (CACML)•Mar 17, 2023•0 citations

Quantum transfer component analysis for domain adaptation

Xi He, Chufan Lyu, Min-Hsiu Hsieh +1 more

Domain adaptation, a crucial sub-field of transfer learning, aims to utilize known knowledge of one data set to accomplish tasks on another data set. In this paper, we perform one of the most representative domain adaptation algorithms, transfer component analysis (TCA), on quantum devices. Two different quantum implementations of this transfer learning algorithm; namely, the linear-algebra-based quantum TCA algorithm and the variational quantum TCA algorithm, are presented. The algorithmic complexity of the linear-algebra-based quantum TCA algorithm is $O(\mathrm{poly}(\log (n_{s} + n_{t})))$, where $n_{s}$ and $n_{t}$ are input sample size. Compared with the corresponding classical algorithm, the linear-algebra-based quantum TCA can be performed on a universal quantum computer with exponential speedup in the number of given samples. Finally, the variational quantum TCA algorithm based on a quantum-classical hybrid procedure, that can be implemented on the near term quantum devices, is proposed.

arXiv:1912.09113•Dec 19, 2019•1 citation

Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages

He, Xi, Feiyu Du, Xiaohan Yu +2 more

The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from two different, but related domains through a quantum information infusion channel. The predicting tasks in the target domain can be achieved with quantum advantages by post-processing quantum measurement results. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup on a universal quantum computer. In addition, the other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices through a variational hybrid quantum-classical procedure. Numerical experiments on the synthetic and handwritten digits datasets demonstrate that the variatioinal transfer fusion (TF) framework can reach state-of-the-art (SOTA) quantum DA method performance.

arXiv:2411.01822•Nov 4, 2024•0 citations

Domain Adaptation Method Inspired by Quantum Convolutional Neural Network

Chunhui Wu, Junhao Pei, Yihua 逸华 Wu 吴 +2 more

Abstract Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation (DA) is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network, named variational quantum domain adaptation (VQDA). The data is first uploaded by a ‘quantum coding module’, then the feature information is extracted by several ‘quantum convolution layers’ and ‘quantum pooling layers’, which is named ‘Feature Extractor’. Subsequently, the labels and the domains of the samples are obtained by the ‘quantum fully connected layer’. With a gradient reversal module, the trained ‘Feature Extractor’ can extract the features that cannot be distinguished from the source and target domains. The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results show that VQDA(with 8 quantum bits) has 91.46% average classification accuracy for DA task between MNIST→USPS (USPS→ MNIST), achieves 91.16% average classification accuracy for gray-scale and color images (with 10 quantum bits), and has 69.25% average classification accuracy on the DA task for color images (also with 10 quantum bits). VQDA achieves a 9.14% improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks. Simultaneously, the parameters scale is reduced to 43% by using VQDA when both quantum and classical DA methods have similar classification accuracies.

Chinese Physics B•Apr 2, 2025•0 citations

Variational Quantum Domain Adaptation

Chunhui Wu, Junhao Pei, Yihua Wu +1 more

Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, a variational quantum domain adaptation method is proposed by using a quantum convolutional neural network, together with a gradient reversal module, and two quantum fully connected layers, named variational quantum domain adaptation(VQDA). The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results demonstrate that, compared to its classical corresponding domain adaptation method, VQDA achieves an average improvement of 4% on the accuracy for MNIST to USPS domain transfer under the same parameter scales. Similarly, for SYNDigits to SVHN domain transfer, VQDA achieves an average improvement of 2% on the accuracy under the same parameter scales.

arXiv:2312.09563•Dec 15, 2023•0 citations

Quantum Machine Learning: Adaptive Transfer Learning

Jincheng Zhang

Quantum Machine Learning (QML) offers a paradigm shift in machine learning, leveraging the unique properties of quantum mechanics to potentially surpass classical algorithms. This paper explores the application of QML to adaptive transfer learning, a technique designed to dynamically adjust model parameters based on the data distribution and feature characteristics. We propose a novel quantum framework for this process, utilizing quantum entanglement to establish a stronger connection between training data and model parameters. This approach aims to address the limitations of traditional transfer learning, particularly in scenarios with complex data distributions and variable feature spaces. The core mechanism focuses on creating a dynamic, self-adjusting model that can generalize better. This work introduces a preliminary quantum algorithm demonstrating the potential for improved transfer learning performance, highlighting the benefits of quantum entanglement in this context.

•Aug 31, 2026•0 citations
Transfer learning (TL) is one of the most important sub-realms in the field of machine learning. The goal is to achieve knowledge transfer from source domain to target domain by utilizing the similarities among data, tasks, or models. Domain adaptation (DA) is actually a type of transductive TL with assumption that both the marginal and the conditional distribution adaptation are different. In this paper, the process of joint distribution adaptation implemented on the universal quantum computer is presented. Compared to the classical joint distribution adaptation, the performance and application scope of the algorithm are promoted by the advantages of the quantum operations. Specifically, the quantum joint distribution adaptation algorithm proposed in our work can be performed exponentially efficient with quantum computing techniques in contrast to the corresponding classical procedure.
Domain adaptation (DA), the sub-realm of the transfer learning, attempts to deal with machine learning tasks on an unprocessed data domain with the different, but related labeled source domain. However, the classical DA can not efficiently deal with the cross-domain tasks in quantum mechanical scenarios. In this paper, the quantum kernel subspace alignment algorithm is proposed to achieve the procedure of DA by extracting the non-linear features with the quantum kernel method and aligning the two domains with the unitary evolution. The method presented in our work can be implemented on the universal quantum computer with the quantum basic linear algebra subroutines. Based on the algorithmic complexity analysis, the procedure of the QKSA can be implemented with at least quadratic quantum speedup compared with the classical DA algorithms.
Domain adaptation, a crucial sub-field of transfer learning, aims to utilize known knowledge of one data set to accomplish tasks on another data set. In this paper, we perform one of the most representative domain adaptation algorithms, transfer component analysis (TCA), on quantum devices. Two different quantum implementations of this transfer learning algorithm; namely, the linear-algebra-based quantum TCA algorithm and the variational quantum TCA algorithm, are presented. The algorithmic complexity of the linear-algebra-based quantum TCA algorithm is O(poly(log⁡(ns+nt)))O(\mathrm{poly}(\log (n_{s} + n_{t})))O(poly(log(ns​+nt​))), where nsn_{s}ns​ and ntn_{t}nt​ are input sample size. Compared with the corresponding classical algorithm, the linear-algebra-based quantum TCA can be performed on a universal quantum computer with exponential speedup in the number of given samples. Finally, the variational quantum TCA algorithm based on a quantum-classical hybrid procedure, that can be implemented on the near term quantum devices, is proposed.
The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from two different, but related domains through a quantum information infusion channel. The predicting tasks in the target domain can be achieved with quantum advantages by post-processing quantum measurement results. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup on a universal quantum computer. In addition, the other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices through a variational hybrid quantum-classical procedure. Numerical experiments on the synthetic and handwritten digits datasets demonstrate that the variatioinal transfer fusion (TF) framework can reach state-of-the-art (SOTA) quantum DA method performance.
Abstract Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation (DA) is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network, named variational quantum domain adaptation (VQDA). The data is first uploaded by a ‘quantum coding module’, then the feature information is extracted by several ‘quantum convolution layers’ and ‘quantum pooling layers’, which is named ‘Feature Extractor’. Subsequently, the labels and the domains of the samples are obtained by the ‘quantum fully connected layer’. With a gradient reversal module, the trained ‘Feature Extractor’ can extract the features that cannot be distinguished from the source and target domains. The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results show that VQDA(with 8 quantum bits) has 91.46% average classification accuracy for DA task between MNIST→USPS (USPS→ MNIST), achieves 91.16% average classification accuracy for gray-scale and color images (with 10 quantum bits), and has 69.25% average classification accuracy on the DA task for color images (also with 10 quantum bits). VQDA achieves a 9.14% improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks. Simultaneously, the parameters scale is reduced to 43% by using VQDA when both quantum and classical DA methods have similar classification accuracies.
Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, a variational quantum domain adaptation method is proposed by using a quantum convolutional neural network, together with a gradient reversal module, and two quantum fully connected layers, named variational quantum domain adaptation(VQDA). The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results demonstrate that, compared to its classical corresponding domain adaptation method, VQDA achieves an average improvement of 4% on the accuracy for MNIST to USPS domain transfer under the same parameter scales. Similarly, for SYNDigits to SVHN domain transfer, VQDA achieves an average improvement of 2% on the accuracy under the same parameter scales.
Quantum Machine Learning (QML) offers a paradigm shift in machine learning, leveraging the unique properties of quantum mechanics to potentially surpass classical algorithms. This paper explores the application of QML to adaptive transfer learning, a technique designed to dynamically adjust model parameters based on the data distribution and feature characteristics. We propose a novel quantum framework for this process, utilizing quantum entanglement to establish a stronger connection between training data and model parameters. This approach aims to address the limitations of traditional transfer learning, particularly in scenarios with complex data distributions and variable feature spaces. The core mechanism focuses on creating a dynamic, self-adjusting model that can generalize better. This work introduces a preliminary quantum algorithm demonstrating the potential for improved transfer learning performance, highlighting the benefits of quantum entanglement in this context.