Quantum classifier with parameterized quantum circuit based on the isolated quantum system

doi:10.19682/j.cnki.1005-8885.2022.2016

中国邮电高校学报(英文) ›› 2022, Vol. 29 ›› Issue (4): 21-31.doi: 10.19682/j.cnki.1005-8885.2022.2016

Quantum classifier with parameterized quantum circuit based on the isolated quantum system

Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin

1. School of Computer Science and Engineering, Central South University, Changsha 410083, China
2. School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411301, China

收稿日期:2022-04-28 修回日期:2022-05-31 接受日期:2022-06-09 出版日期:2022-08-31 发布日期:2022-08-31
通讯作者: Mu Shuai, Li Qin, E-mails: csumushuai@csu.edu.cn, liqin@xtu.edu.cn E-mail:csumushuai@csu.edu.cn, liqin@xtu.edu.cn
基金资助:
This work was supported by the National Natural Science Foundation of China (61972418, 61872390), the Natural
Science Foundation of Hunan Province (2020JJ4750), the Special Foundation for Distinguished Young Scientists of
Changsha (kq1905058), China Computer Federation (CCF) -Baidu Open Fund (CCF-BAIDUOF2021031), the Fundamental
Research Funds for the Central Universities of Central South University, China (2022XQLH014).

Quantum classifier with parameterized quantum circuit based on the isolated quantum system

Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin

1. School of Computer Science and Engineering, Central South University, Changsha 410083, China
2. School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411301, China

Received:2022-04-28 Revised:2022-05-31 Accepted:2022-06-09 Online:2022-08-31 Published:2022-08-31
Contact: Mu Shuai, Li Qin, E-mails: csumushuai@csu.edu.cn, liqin@xtu.edu.cn E-mail:csumushuai@csu.edu.cn, liqin@xtu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61972418, 61872390), the Natural
Science Foundation of Hunan Province (2020JJ4750), the Special Foundation for Distinguished Young Scientists of
Changsha (kq1905058), China Computer Federation (CCF) -Baidu Open Fund (CCF-BAIDUOF2021031), the Fundamental
Research Funds for the Central Universities of Central South University, China (2022XQLH014).

摘要/Abstract

摘要： It is a critical challenge for quantum machine learning to classify the datasets accurately. This article develops a quantum classifier based on the isolated quantum system (QC-IQS) to classify nonlinear and multidimensional datasets. First, a model of QC-IQS is presented by creating parameterized quantum circuits (PQCs) based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system. Then, a parameterized quantum classification algorithm (QCA) is designed to calculate the classification results by updating the loss function until it converges. Finally, the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets. The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data. Moreover, QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets. It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.

关键词: quantum classifier, quantum classification, isolated quantum system, parameterized quantum circuit, Hamiltonian

Abstract: It is a critical challenge for quantum machine learning to classify the datasets accurately. This article develops a quantum classifier based on the isolated quantum system (QC-IQS) to classify nonlinear and multidimensional datasets. First, a model of QC-IQS is presented by creating parameterized quantum circuits (PQCs) based on the decomposing of unitary operators with the Hamiltonian in the isolated quantum system. Then, a parameterized quantum classification algorithm (QCA) is designed to calculate the classification results by updating the loss function until it converges. Finally, the experiments on nonlinear random number datasets and Iris datasets are designed to demonstrate that the QC-IQS model can handle and generate accurate classification results on different kinds of datasets. The experimental results reveal that the QC-IQS is adaptive and learnable to handle different types of data. Moreover, QC-IQS compensates the issue that the accuracy of previous quantum classifiers declines when dealing with diverse datasets. It promotes the process of novel data processing with quantum machine learning and has the potential for more comprehensive applications in the future.

Key words: quantum classifier, quantum classification, isolated quantum system, parameterized quantum circuit, Hamiltonian

中图分类号:

TP391

Shi Jinjing, Wang Wenxuan, Xiao Zimeng, Mu Shuai, Li Qin. Quantum classifier with parameterized quantum circuit based on the isolated quantum system[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(4): 21-31.

参考文献

References
[1] GHEORGHIU A, KAPOURNIOTIS T, KASHEFI E. Verification of quantum computation: An overview of existing approaches. Theory of Computing Systems, 2019, 63(4): 715 -808.
[2] ZHONG H S, WANG H, DENG Y H, et al. Quantum computational advantage using photons. Science, 2020,
370(6523): 1460 -1463.
[3] WU Y L, BAO W S, CAO S R, et al. Strong quantum computational advantage using a superconducting quantum
processor. Physical Review Letters, 2021, 127(18): Article 180501.
[4] SHI J J, CHEN H, ZHOU F, et al. Quantum blind signature scheme with cluster states based on quantum walk cryptosystem. International Journal of Theoretical Physics, 2019, 58(4): 1337 -1349.
[5] SHI J J, SHI R H, PENG X Q, et al. Quantum relay cooperative communication via space-time transmission. Quantum Information and Computation, 2015, 15(7/8): 660 -676.
[6] SHOR P W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Review, 1999, 41(2): 303 -332.
[7] GROVER L K. A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC'96), 1996, May 22 - 24, Philadelphia, PA, USA. New York, NY, USA: ACM, 1996: 212 -219.
[8] BENEDETTI M, LLOYD E, SACK S, et al. Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 2019, 4(4): Article 043001.
[9] FEDOROV D A, PENG B, GOVIND N, et al. VQE method: A short survey and recent developments. Materials Theory, 2022, 6(1): Article 2812.
[10] SIM S, JOHNSON P D, ASPURU-GUZIK A. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2019, 2(12): Article 1900070.
[11] SHI J J, TANG Y Z, LU Y H, et al. Quantum circuit learning with parameterized boson sampling. IEEE Transactions on Knowledge and Data Engineering, 2021, Early Access Article, DOI: 10.1109/TKDE.2021.3095103.
[12] BIAMONTE J, WITTEK P, PANCOTTI N, et al. Quantum machine learning. Nature, 2017, 549(7671): 195 -202.
[13] SHI J J, LU Y H, FENG Y Y, et al. A quantum hash function with grouped coarse-grained boson sampling. Quantum Information Processing, 2022, 21(2): Article 73.
[14] SHI J J, LI Z H, LAI W, et al. Two end-to-end quantum-inspired deep neural networks for text classification. IEEE
Transactions on Knowledge and Data Engineering, 2021, Early Access Article, DOI: 10.1109/TKDE.2021.3130598.
[15] LI G X, SONG Z X, WANG X. VSQL: Variational shadow quantum learning for classification. Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), 2021, Feb 2 - 9, Vancouver, Canada. Menlo Park, CA, USA: American Association for Artificial Intelligence (AAAI), 2021: 8357 -8365.

[16] LI Y C, ZHOU R G, XU R Q, et al. A quantum deep convolutional neural network for image recognition. Quantum
Science and Technology, 2020, 5(4): Article 044003.
[17] REBENTROST P, MOHSENI M, LLOYD S. Quantum support vector machine for big data classification. Physical Review Letters, 2014, 113(13): Article 130503.
[18] CHEN C C, WATABE M, SHIBA K, et al. On the expressibility and overfitting of quantum circuit learning. ACM Transactions on Quantum Computing, 2021, 2(2): Article 8.
[19] HUR T, KIM L, PARK D K. Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence, 2022, 4(1): Article 4.
[20] ZIDAN M, ABDEL-ATY A H, EL-SHAFEI M, et al. Quantum classification algorithm based on competitive learning neural network and entanglement measure. Applied Sciences, 2019, 9(7): Article 1277.
[21] ABDEL-ATY A H, KADRY H, ZIDAN M, et al. A quantum classification algorithm for classification incomplete patterns based on entanglement measure. Journal of Intelligent and Fuzzy Systems, 2020, 38(3): 2809 -2816.
[22] ABLAYEV F, ABLAYEV M, HUANG J Z, et al. On quantum methods for machine learning problems part II: Quantum classification algorithms. Big Data Mining and Analytics, 2019, 3(1): 56 -67.
[23] HUBREGTSEN T, PICHLMEIER J, STECHER P, et al. Evaluation of parameterized quantum circuits: On the relation between classification accuracy, expressibility, and entangling capability. Quantum Machine Intelligence, 2021, 3(1): Article 9.
[24] EASTIN B, KNILL E. Restrictions on transversal encoded quantum gate sets. Physical Review Letters, 2009, 102(11): Article 110502.
[25] LI S S, LONG G L, BAI F S, et al. Quantum computing. Proceedings of the National Academy of Sciences, 2001,
98(21): 11847 -11848.
[26] O'BRIEN J L. Optical quantum computing. Science, 2007, 318(5856): 1567 -1570.
[27] SCHMIDT-KALER F, HAFFNER H, RIEBE M, et al. Realization of the Cirac-Zoller controlled-NOT quantum gate.
Nature, 2003, 422(6930): 408 -411.
[28] ALLAHVERDYAN A E, BALIAN R, NIEUWENHUIZEN T M. Understanding quantum measurement from the solution of dynamical models. Physics Reports, 2013, 525(1): 1 -166.
[29] GRING M, KUHNERT M, LANGEN T, et al. Relaxation and prethermalization in an isolated quantum system. Science, 2012, 337(6100): 1318 -1322.
[30] NEILL C, ROUSHAN P, FANG M, et al. Ergodic dynamics and thermalization in an isolated quantum system. Nature Physics, 2016, 12(11): 1037 -1041.
[31] KATO T. On the adiabatic theorem of quantum mechanics. Journal of the Physical Society of Japan, 1950, 5(6): 435 -439.
[32] AMBAINIS A, NAYAK A, TA-SHMA A, et al. Dense quantum coding and quantum finite automata. Journal of the ACM, 2002, 49(4): 496 -511.
[33] FU Y G, DING M Y, ZHOU C P. Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2012, 42(2): 511 -526.
[34] RUDOLPH T. Simple encoding of a quantum circuit amplitude as a matrix permanent. Physical Review A, 2009, 80(5): Article 054302.
[35] HUANG P, HUANG J Z, ZHANG Z S, et al. Quantum key distribution using basis encoding of Gaussian-modulated coherent states. Physical Review A, 2018, 97(4): Article 042311.
[36] OSTASZEWSKI M, GRANT E, BENEDETTI M. Structure optimization for parameterized quantum circuits. Quantum,
2021(5): Article 391.
[37] FRITZSCH F, PROSEN T. Eigenstate thermalization in dual-unitary quantum circuits: Asymptotics of spectral functions. Physical Review E, 2021, 103(6): Article 062133.
[38] BRECHT T, PFAFF W, WANG C, et al. Multilayer microwave integrated quantum circuits for scalable quantum computing. NPJ Quantum Information, 2016, 2(1): Article 16002.
[39] LI P C, SHI T, LU A P, et al. Quantum circuit design for several morphological image processing methods. Quantum Information Processing, 2019, 18(12): Article 364.
[40] MI X, ROUSHAN P, QUINTANA C, et al. Information scrambling in quantum circuits. Science, 2021, 374(6574): 1479 -1483.
[41] XIANG Z L, ASHHAB S, YOU J Q, et al. Hybrid quantum circuits: Superconducting circuits interacting with other quantum systems. Reviews of Modern Physics, 2013, 85(2): Article 2013.
[42] SHOR P W. Quantum computing. Documenta Mathematica: Extra Volume ICM, 1998, 1: 467 -486.
[43] WANG X Y, ZHAO S H, DONG C, et al. Orbital angular momentum-encoded measurement device independent quantum key distribution under atmospheric turbulence. Quantum Information Processing, 2019, 18(10): Article 304.
[44] LAROSE R, COYLE B. Robust data encodings for quantum classifiers. Physical Review A, 2020, 102(3): Article 032420.
[45] KUSUMOTO T, MITARAI K, FUJII K, et al. Experimental quantum kernel trick with nuclear spins in a solid. NPJ Quantum Information, 2021, 7(1): Article 94.
[46] BOTTOU L. Stochastic gradient descent tricks. MONTAVON G, ORR G B, MULLER K R(eds). Neural Networks: Tricks of the Trade. LNCS 7700. Berlin, Germany: Springer, 2012: 421 -436.
[47] FARHI E, NEVEN H. Classification with quantum neural networks on near term processors, ArXiv Preprint, arXiv: 1802.06002, 2018.
[48] PANDEY A, RAMESH V. Quantum computing for big data analysis. Indian Journal of Science, 2015, 14(43): 98 -104.

Quantum classifier with parameterized quantum circuit based on the isolated quantum system

Quantum classifier with parameterized quantum circuit based on the isolated quantum system

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Guo Xiangbo, Wang Jian, Huang Mengjie, Wang Minghui, Yang Jian, Yu Yongtao. Deep knowledge tracking algorithm based on forgetting law[J]. 中国邮电高校学报(英文版), 2023, 30(1): 17-27.
[2]	Wang Xianlun, Wang Guangyu, Cui Yuxia. Facial expression recognition based on improved ResNet[J]. 中国邮电高校学报(英文版), 2023, 30(1): 28-38.
[3]	Wu Hongxin, Lin Zhijian, Chen Pingping, Chen Feng. Joint partial computation offloading and resource allocation in MEC-enable networks[J]. 中国邮电高校学报(英文版), 2023, 30(1): 80-86.
[4]	Kong Chao, Ou Weihua, Gong Xiaofeng, Li Weian, Han Jie, Yao Yi, Xiong Jiahao. Face anti-spoofing based on multi-modal and multi-scale features fusion [J]. 中国邮电高校学报(英文), 2022, 29(6): 73-82.
[5]	张函井音吉赵永利. FS-LSTM: sales forecasting in e-commerce on feature selection [J]. 中国邮电高校学报(英文版), 2022, 29(5): 92-98.
[6]	Liu Hailing, Zhang Jie, Qin Sujuan, Gao Fei. Quantum algorithm for soft margin support vector machine with hinge loss function[J]. 中国邮电高校学报(英文), 2022, 29(4): 32-41.
[7]	Wang Jian, Qiao Kuoyuan, Yuan Yanlei, Liu Xiaole, Yang Jian. Adaptive learning path recommendation model for examination-oriented education[J]. 中国邮电高校学报(英文), 2022, 29(4): 77-88.
[8]	Meng Wei, Wang Liting, Lu Meng. Summary of research on recommendation system based on serendipity[J]. 中国邮电高校学报(英文), 2022, 29(4): 89-105.
[9]	Jia Wei, Gong Chao. Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system[J]. 中国邮电高校学报(英文), 2022, 29(3): 1-14.
[10]	Song Yue, Wu Chengmao, Tian Xiaoping, Song Qiuyu. Enhanced kernel-based fuzzy local information clustering integrating neighborhood membership [J]. 中国邮电高校学报(英文版), 2021, 28(6): 65-81.
[11]	Xue Chenzi, Wei Yifei, Zhang Yong. Performance optimization for smart grid blockchain integrated with fog computing using DDQN[J]. 中国邮电高校学报(英文版), 2021, 28(2): 68-78.
[12]	Guo Hairu, Meng Xueyao, Liu Yongli, Liu Shen. Improved HHO algorithm based on good point set and nonlinear convergence formula[J]. 中国邮电高校学报(英文版), 2021, 28(2): 48-67.
[13]	吴成茂曹卓. Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation [J]. 中国邮电高校学报(英文版), 2021, 28(1): 24-40.
[14]	山蕊蒋林邓军勇崔朋飞张玉婷吴皓月谢晓燕. Parallel design of convolutional neural networks for remote sensing images object recognition based on data-driven array processor [J]. 中国邮电高校学报(英文版), 2020, 27(6): 87-100.
[15]	汪昭颖, 周军华, 廖中华, 翟翔, 张连平. Semantic segmentation of track image based on deep neural network[J]. 中国邮电高校学报(英文版), 2020, 27(5): 23-33.