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Chin. Phys. B, 2024, Vol. 33(4): 040304    DOI: 10.1088/1674-1056/ad02e7
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Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism

Run-Sheng Zhao(赵润盛)1, Hong-Yang Ma(马鸿洋)1, Tao Cheng(程涛)2, Shuang Wang(王爽)2, and Xing-Kui Fan(范兴奎)1,†
1 School of Sciences, Qingdao University of Technology, Qingdao 266033, China;
2 School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
Abstract  Readout errors caused by measurement noise are a significant source of errors in quantum circuits, which severely affect the output results and are an urgent problem to be solved in noisy-intermediate scale quantum (NISQ) computing. In this paper, we use the bit-flip averaging (BFA) method to mitigate frequent readout errors in quantum generative adversarial networks (QGAN) for image generation, which simplifies the response matrix structure by averaging the qubits for each random bit-flip in advance, successfully solving problems with high cost of measurement for traditional error mitigation methods. Our experiments were simulated in Qiskit using the handwritten digit image recognition dataset under the BFA-based method, the Kullback—Leibler (KL) divergence of the generated images converges to 0.04, 0.05, and 0.1 for readout error probabilities of p=0.01, p=0.05, and p=0.1, respectively. Additionally, by evaluating the fidelity of the quantum states representing the images, we observe average fidelity values of 0.97, 0.96, and 0.95 for the three readout error probabilities, respectively. These results demonstrate the robustness of the model in mitigating readout errors and provide a highly fault tolerant mechanism for image generation models.
Keywords:  readout errors      quantum generative adversarial networks      bit-flip averaging method      fault tolerant mechanisms  
Received:  17 July 2023      Revised:  12 October 2023      Accepted manuscript online:  13 October 2023
PACS:  03.67.Ac (Quantum algorithms, protocols, and simulations)  
  03.67.Pp (Quantum error correction and other methods for protection against decoherence)  
  03.67.Lx (Quantum computation architectures and implementations)  
Fund: Project supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF049) and Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022LLZ012 and ZR2021LLZ001).
Corresponding Authors:  Xing-Kui Fan     E-mail:  fanxingkui@126.com

Cite this article: 

Run-Sheng Zhao(赵润盛), Hong-Yang Ma(马鸿洋), Tao Cheng(程涛), Shuang Wang(王爽), and Xing-Kui Fan(范兴奎) Quantum generative adversarial networks based on a readout error mitigation method with fault tolerant mechanism 2024 Chin. Phys. B 33 040304

[1] Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2017 Nature 549 195
[2] Bartolucci S, Birchall P, Bombín H, Cable H, Dawson C, Segovia M G, Johnston E, Kieling K, Nickerson N, Pant M, Pastawski F, Rudolph T and Sparrow C 2023 Nat. Commun. 14 912
[3] Stajic J 2023 Science 339 1163
[4] Schuld M and Killoran N 2019 Phys. Rev. Lett. 122 040504
[5] Zhou N R, Zhang T F, Xie X W and Wu J Y 2023 Signal Processing:Image Communication 110 116891
[6] Niu M Y, Zlokapa A, Broughton M, Boixo S, Mohseni M, Smelyanskyi V and Neven H 2022 Phys. Rev. Lett. 128 220505
[7] Lloyd S and Weedbrook C 2018 Phys. Rev. Lett. 121 040502
[8] Stein S A, Baheri B, Chen D, Mao Y, Guan Q, Li A, Fang B and Xu S 2021 2021 IEEE International Conference on Quantum Computing and Engineering (QCE) pp. 71——81
[9] Huang H L, Du Y, Gong M, et al. 2021 Phys. Rev. Appl. 16 024051
[10] Zoufal C, Lucchi A and Woerner S 2019 npj Quantum Information 5 103
[11] Preskill J 2018 Quantum 2 79
[12] Saki A A, Alam M and Ghosh S 2021 2021 22nd International Symposium on Quality Electronic Design (ISQED) pp. 186-191
[13] Harrow A W and Montanaro A 2017 Nature 549 203
[14] Maciejewski F B, Baccari F, Zimborás Z and Oszmaniec M 2021 Quantum 5 464
[15] Nachman B, Urbanek M, Jong W A D and Bauer C W 2020 npj Quantum Information 6 84
[16] Berg E V D, Minev Z K and Temme K 2022 Phys. Rev. A 105 032620
[17] Livingston W P, Blok M S, Flurin E, Dressel J, Jordan A N and Siddiqi I 2022 Nat. Commun. 13 2307
[18] He A, Nachman B, Jong W A D and Bauer C W 2020 Phys. Rev. A 102 012426
[19] Pascuzzi V R, He A, Bauer C W, Jong W A D and Nachman B 2022 Phys. Rev. A 105 042406
[20] Bravyi S, Sheldon S, Kandala A, Mckay D C and Gambetta J M 2021 Phys. Rev. A 103 042605
[21] Borras K, Chang S Y, Funcke L, Grossi M, Hartung T, Jansen K, Kruecker D, Kühn S, Rehm F, Tüysüz C and Vallecorsa S 2023 J. Phys.:Conf. Series 2438 p. 012093
[22] Geller M R and Sun M 2021 Quantum 6 025009
[23] Smith A W R, Khosla K E, Self C N and Kim MS 2021 Science Advances 7 eabi8009
[24] Wang C, Li X, Xu H, et al. 2022 npj Quantum Information 8 3
[25] Daley A J, Bloch I, Kokail C, Flannigan S, Pearson N, Troyer M and Zoller P 2022 Nature 607 667
[26] Gyongyosi L and Imre S 2019 Computer Science Review 31 51-71
[27] Temme K, Bravyi S and Gambetta J M 2017 Phys. Rev. Lett. 119 180509
[28] Ostaszewski M, Grant E and Benedetti M 2021 Quantum 5 391
[29] Chu J, Li D, Yang X P, Song, S Q, Han Z K, Yang Z, Dong Y Q, Zheng W, Wang Z M, Yu X M, Lan D, Tan X S and Yu Y 2020 Phys. Rev. Appl. 13 064012
[30] Houssein E H, Abohashima Z, Elhoseny M and Mohamed W M 2022 Expert Systems with Applications 2022 116512
[31] Postler L, Heuβen S, Pogorelov I, Rispler M, Feldker T, Meth M, Marciniak C D, Stricker R, Ringbauer M, Blatt R, Philipp S, Markus M and Thomas M 2022 Nature 605 675
[32] Funcke L, Hartung T, Jansen K, Kühn S, Stornati P and Wang X Y 2022 Phys. Rev. A 105 062404
[33] Fisher M P A, Khemani V, Nahum A and Vijay S 2018 Annual Review of Condensed Matter Physics 14 335
[34] Goodfellow I, Abadie P J, Mirza M, Xu B, Farley W D, Ozair S, Courville A and Bengio Y 2020 Communications of the ACM 63 139-144
[35] Zeng J F, Wu Y F, Liu J G, Wang L and Hu J P 2019 Phys. Rev. A 99 052306
[36] Paszke A, Gross S, Massa F, et al 2019 Advances in neural information processing systems 32
[37] Imambi S, Prakash K B and Kanagachidambaresan G R 2021 Programming with TensorFlow:Solution for Edge Computing Applications 87 104
[38] Alexander T, Kanazawa N, Egger D J, Capelluto L, Wood C J, Abhari A J and McKay D C 2020 Quantum Science and Technology 5 044006
[39] Shaik H E, Rangaswamy N 2020 2020 5th International conference on computing, communication and security (ICCCS) pp. 1——6
[40] Dheeru D and Casey G 2017 UCI machine learning repository
[41] Yu Y M, Gao J, Mu X Y and Wang S M 2023 Quantum Information Processing 22 180
[42] Wang Y N, Song Z Y, Ma Y L, Hua N and Ma H Y 2021 Acta Phys. Sin. 70 230302 (in Chinese)
[43] Jiang Y Y, Chu P C, Zhang W B and Ma H Y 2022 Chin. Phys. B 31 040307
[44] Chiribella G, D'Ariano G M and Perinotti P 2008 Phys. Rev. Lett. 101 060401
[45] Liu J G and Wang L 2018 Phys. Rev. A 98 062324
[46] Facchinei F and Kanzow C 2007 4OR 5 173
[47] Johnstun S, Van H and Jean-François 2021 American Journal of Physics 89 935
[48] Chang S Y, Agnew E, Combarro E, Grossi M, Herbert S and Vallecorsa S 2023 J. Phys.:Conf. Series 2438 012062
[49] McArdle S, Yuan X and Benjamin S 2019 Phys. Rev. Lett. 122 180501
[50] Cattaneo M, Rossi M A and García-Pérez G 2023 PRX quantum 4 010324
[51] LaRose R, Mari A, Kaiser S, Karalekas P J, Alves A A, Czarnik P, Mandouh M E, Gordon M H, Hindy Y, Robertson A, Thakre P, Wahl M, Samuel D, Mistri R, Tremblay M, Gardner N, Stemen N T, Shammah N and Zeng W J 2022 Quantum 6 774
[52] Bu Y, Zou S, Liang Y and Veeravalli V V 2018 IEEE Transactions on Information Theory 64 2648
[53] Lu T A, Qiu Z H, Zhang Z B and Zhong J G 2020 Optics and Lasers in Engineering 134 106301
[54] Duro D C, Franklin S E and Dub M G 2012 Remote sensing of environment 118 259
[55] Cozzini M, Ionicioiu R and Zanardi P 2007 Phys. Rev. B 76 104420
[56] Rahman A U, Haddadi S, Pourkarimi M R and Ghominejad M 2022 Laser Physics Letters 19 035204
[57] Huang H Y, Broughton M, Mohseni M, Babbush R, Boixo S, Neven H and McClean J R 2021 Bulletin of the American Physical Society 12 2631
[58] An Z and Zhou DL 2019 Europhys. Lett. 126 60002
[59] Fawzi O, Grospellier A and Leverrier A 2020 Communications of the ACM 64 106
[60] Liang Y C, Yeh Y H, Mendonça P E, Teh R Y, Reid M D and Drummond P D 2019 Reports on Progress in Physics 82 076001
[61] Liu Y C, Arunachalam S and Temme K 2021 Nat. Phys. 17 1013
[62] Ajagekar A and You F Q 2021 Applied Energy 303 117628
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