|
|
|
Single-qubit quantum classifier based on gradient-free optimization algorithm |
| Anqi Zhang(张安琪)1, Kelun Wang(王可伦)1, Yihua Wu(吴逸华)1, and Sheng-Mei Zhao(赵生妹)1,2,† |
1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing 210003, China |
|
|
|
|
Abstract A single-qubit quantum classifier (SQC) based on a gradient-free optimization (GFO) algorithm, named the GFO-based SQC, is proposed to overcome the effects of barren plateaus caused by quantum devices. Here, a rotation gate $R_{X}(\phi)$ is applied on the single-qubit binary quantum classifier, and the training data and parameters are loaded into $\phi$ in the form of vector multiplication. The cost function is decreased by finding the value of each parameter that yields the minimum expectation value of measuring the quantum circuit. The algorithm is performed iteratively for all parameters one by one until the cost function satisfies the stop condition. The proposed GFO-based SQC is demonstrated for classification tasks in Iris and MNIST datasets and compared with the Adam-based SQC and the quantum support vector machine (QSVM). Furthermore, the performance of the GFO-based SQC is discussed when the rotation gate in the quantum device is under different types of noise. The simulation results show that the GFO-based SQC can reach a high accuracy in reduced time. Additionally, the proposed GFO algorithm can quickly complete the training process of the SQC. Importantly, the GFO-based SQC has a good performance in noisy environments.
|
Received: 03 October 2022
Revised: 16 January 2023
Accepted manuscript online: 31 January 2023
|
|
PACS:
|
03.67.Ac
|
(Quantum algorithms, protocols, and simulations)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 62375140) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX19 0900). |
Corresponding Authors:
Sheng-Mei Zhao
E-mail: zhaosm@njupt.edu.cn
|
Cite this article:
Anqi Zhang(张安琪), Kelun Wang(王可伦), Yihua Wu(吴逸华), and Sheng-Mei Zhao(赵生妹) Single-qubit quantum classifier based on gradient-free optimization algorithm 2023 Chin. Phys. B 32 100308
|
[1] Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2017 Nature 549 195
[2] Benedetti M, Lloyd E, Sack S and Fiorentini M 2019 Quantum Sci. Technol. 4 043001
[3] Teresa S L, Juan R R and David Z 2022 Phys. Rev. A 105 042432
[4] Naoko K M and Kei M 2021 Phys. Rev. A 104 062411
[5] Wang X M, Zhang A Q, Xu P and Zhao S M 2021 Chin. Phys. B 30 030307
[6] Hou Y Y, Li J, Chen X B and Tian Y 2022 Chin. Phys. B 31 030304
[7] Wan K H, Dahlsten O, Kristj' ansson H, Gardner R and Kim M S 2017 npj Quantum Inform. 3 36
[8] Torrontegui E and Garcia-Ripoll J J 2019 Europhys. Lett. 125 30004
[9] Killoran N, Bromley T R, Arrazola J M, Schuld M, Quesada N and Lloyd S 2019 Phys. Rev. Research 1 033063
[10] Mari A, Bromley T R, Izaac J, Schuld M and Killoran N 2020 Quantum 4 340
[11] Schuld M and Killoran N 2019 Phys. Rev. Lett. 122 040504
[12] Gilyén A, Arunachalam S and Wiebe N 2019 Proceedings of the 2019 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) 1425
[13] Campos E, Nasrallah A and Biamonte J 2021 Phys. Rev. A 103 032607
[14] Schuld M, Bocharov A, Svore K M and Wiebe N 2020 Phys. Rev. A 101 032308
[15] Adhikary S, Dangwal S and Bhowmik D 2020 Quantum Inf. Process. 19 89
[16] Chalumuri A, Kune R and Manoj B S 2021 Quantum Inf. Process. 20 119
[17] Chen S Y C, Huang C M, Hsing C W and Kao Y J 2021 Mach. Learn-Sci. Techn. 2 045021
[18] Bhatia A S, Saggi M K, Kumar A and Jain S 2019 arXiv:1905.01426
[19] Adhikary S 2020 arXiv:2006.13302
[20] Zhang A Q, He X Y and Zhao S M 2022 arXiv:2203.04097
[21] Adri' an P S, Alba C L, Elies G F and José I L 2020 Quantum 4 226
[22] Holmes Z, Sharma K, Cerezo M and Coles P J 2022 PRX Quantum 3 010313
[23] Skolik A, McClean J R, Mohseni M, Smagt P V D and Leib M 2021 Quant. Mach. Intell. 3 5
[24] Iannelli G, Jansen K 2021 arXiv:2112.00426
[25] Ostaszewski M, Grant E and Benedetti M 2021 Quantum 5 391
[26] Pesah A, Cerezo M, Wang S, Volkoff T, Sornborger A T and Coles P J 2021 Phys. Rev. X 11 041011
[27] Bergholm V, Izaac J, Schuld M, et al. 2018 arXiv:1811.04968
[28] Comelli P, Ferragina P, Granieri M N and Stabile F 1995 IEEE Transactions on Vehicular Technology 44 790
[29] Nielsen M A and Chuang I 2002 American Journal of Physics 70 558 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
