Quantum kernel subspace alignment for unsupervised domain adaptation
Authors: 
Xi He, Feiyu DuAuthors Info & Claims
Xi He, Feiyu DuAuthors Info & ClaimsAbstract
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.
References
[1]
Esma Aïmeur, Gilles Brassard, and Sébastien Gambs. 2013. Quantum speed-up for unsupervised learning. Machine Learning 90, 2 (2013), 261–287.
[2]
Mohammad H Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko. 2018. Quantum boltzmann machine. Physical Review X 8, 2 (2018), 021050.
[3]
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, 2019. Quantum supremacy using a programmable superconducting processor. Nature 574, 7779 (2019), 505–510.
[4]
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning. Nature 549, 7671 (2017), 195–202.
[5]
Iris Cong and Luming Duan. 2016. Quantum discriminant analysis for dimensionality reduction and classification. New Journal of Physics 18, 7 (2016), 073011.
[6]
Daoyi Dong, Chunlin Chen, Hanxiong Li, and Tzyh-Jong Tarn. 2008. Quantum reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, 5 (2008), 1207–1220.
[7]
Vedran Dunjko, Jacob M Taylor, and Hans J Briegel. 2017. Advances in quantum reinforcement learning. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 282–287.
[8]
Aram W Harrow, Avinatan Hassidim, and Seth Lloyd. 2009. Quantum algorithm for linear systems of equations. Physical review letters 103, 15 (2009), 150502.
[9]
Vojtěch Havlíček, Antonio D Córcoles, Kristan Temme, Aram W Harrow, Abhinav Kandala, Jerry M Chow, and Jay M Gambetta. 2019. Supervised learning with quantum-enhanced feature spaces. Nature 567, 7747 (2019), 209–212.
[10]
Xi He. 2020. Quantum correlation alignment for unsupervised domain adaptation. Physical Review A 102, 3 (2020), 032410.
[11]
Xi He. 2020. Quantum subspace alignment for domain adaptation. Physical Review A 102, 6 (2020), 062403.
[12]
Xi He, Feiyu Du, Mingyuan Xue, Xiaogang Du, Tao Lei, and AK Nandi. 2023. Quantum classifiers for domain adaptation. Quantum Information Processing 22, 2 (2023), 105.
[13]
Xi He, Li Sun, Chufan Lyu, and Xiaoting Wang. 2019. Quantum locally linear embedding. arXiv preprint arXiv:1910.07854 (2019).
[14]
Michael A. Nielsen and Isaac L. Chuang. 2011. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press. https://www.amazon.com/Quantum-Computation-Information-10th-Anniversary/dp/1107002176?SubscriptionId=AKIAIOBINVZYXZQZ2U3A&tag=chimbori05-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=1107002176
[15]
Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345–1359.
[16]
Lorien Y Pratt. 1993. Discriminability-based transfer between neural networks. In Advances in neural information processing systems. 204–211.
[17]
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd. 2014. Quantum support vector machine for big data classification. Physical review letters 113, 13 (2014), 130503.
[18]
Patrick Rebentrost, Adrian Steffens, Iman Marvian, and Seth Lloyd. 2018. Quantum singular-value decomposition of nonsparse low-rank matrices. Physical review A 97, 1 (2018), 012327.
[19]
Jonathan Romero, Jonathan P Olson, and Alan Aspuru-Guzik. 2017. Quantum autoencoders for efficient compression of quantum data. Quantum Science and Technology 2, 4 (2017), 045001.
[20]
Maria Schuld, Alex Bocharov, Krysta M Svore, and Nathan Wiebe. 2020. Circuit-centric quantum classifiers. Physical Review A 101, 3 (2020), 032308.
[21]
Maria Schuld and Nathan Killoran. 2019. Quantum machine learning in feature hilbert spaces. Physical review letters 122, 4 (2019), 040504.
[22]
Maria Schuld and Francesco Petruccione. 2018. Quantum ensembles of quantum classifiers. Scientific reports 8, 1 (2018), 1–12.
[23]
Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. 2016. Prediction by linear regression on a quantum computer. Physical Review A 94, 2 (2016), 022342.
[24]
Xiaoming Sun, Guojing Tian, Shuai Yang, Pei Yuan, and Shengyu Zhang. 2023. Asymptotically optimal circuit depth for quantum state preparation and general unitary synthesis. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2023).
[25]
Nathan Wiebe, Daniel Braun, and Seth Lloyd. 2012. Quantum algorithm for data fitting. Physical review letters 109, 5 (2012), 050505.
[26]
Nathan Wiebe, Ashish Kapoor, and Krysta M Svore. 2018. Quantum nearest-neighbor algorithms for machine learning. Quantum Information and Computation 15 (2018).
Index Terms
- Quantum kernel subspace alignment for unsupervised domain adaptation
Recommendations
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision
AbstractMachine learning has become a ubiquitous and effective technique for data processing and classification. Furthermore, due to the superiority and progress of quantum computing in many areas (e.g., cryptography, machine learning, ...
Highlights- Organize the most recent research works to pave the way for QML researchers.
- ...
VQNE: Variational Quantum Network Embedding with Application to Network Alignment
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningLearning of network embedding with vector-based node representation has attracted wide attention over the decade. It differs from the general setting of graph node embedding whereby the node attributes are also considered and yet may incur privacy ...
Supercomputing leverages quantum machine learning and Grover’s algorithm
AbstractThe complexity of searching algorithms in classical computing is a classic problem and a research area. Quantum computers and quantum algorithms can efficiently compute some classically hard problems. In addition, quantum machine learning ...
Comments
Information & Contributors
Information
Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Natural Science Basic Research Program of Shaanxi
Conference
CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
Acceptance Rates
CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 42Total Downloads
- Downloads (Last 12 months)30
- Downloads (Last 6 weeks)1
Reflects downloads up to 14 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format