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Quantum Information Processing
Published in: Quantum Information Processing 2/2024

01-02-2024

Denoising quantum mixed states using quantum autoencoders

Author: Ming-Ming Wang

Published in: Quantum Information Processing | Issue 2/2024

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Abstract

The presence of noise affects the process of quantum computing and quantum communication, and quantum autoencoders (QAEs) provide a new solution for dealing with this problem. Previous study has shown that QAEs could denoise pure quantum states subject to spin-flip errors and random unitary noise (Bondarenko and Feldmann Phys Rev Lett 124: 130502, 2020). However, avoiding or reducing the interference of noise on mixed states remains an interesting problem in quantum communication and quantum computing. In this paper, the denoising effect of QAEs for mixed states is studied. We investigate the denoising effect of QAEs for a specific type of mixed states in four types of noise usually encountered in the real world, i.e., the bit-flip, the phase-flip, the depolarizing, and the amplitude-damping noise. Simulation results show that the QAEs can significantly denoise four types of noise on mixed states with different noisy and mixed parameters.
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Literature
1.
2.
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, pp. 45–85. MIT Press, Cambridge (2016)
3.
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11 (2017)CrossRef
4.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M.S.: Deep learning for visual understanding: a review. Neurocomputing 187, 27 (2016)CrossRef
5.
Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Comput. 26(5), 1484 (1997)MathSciNetCrossRef
6.
Grover, L.K.: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325 (1997)ADSCrossRef
7.
8.
Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56(2), 172 (2015)ADSCrossRef
9.
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195 (2017)ADSCrossRefPubMed
10.
11.
Ronald, C.: Quantum learning. In: New Directions in Cognitive Science: Proceedings of the International Symposium, pp. 4–9 (1995)
12.
Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)ADSCrossRefPubMed
13.
Li, Z., Liu, X., Xu, N., Du, J.: Experimental realization of a quantum support vector machine. Phys. Rev. Lett. 114(14), 140504 (2015)ADSCrossRefPubMed
14.
Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631 (2014)CrossRef
15.
Aïmeur, E., Brassard, G., Gambs, S.: Machine learning in a quantum world. In: Conference of the Canadian Society for Computational Studies of Intelligence, pp. 431–442 (2006)
16.
17.
Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum algorithms for supervised and unsupervised machine learning, arXiv preprint, arXiv:​1307.​0411, (2013)
18.
Behrman, E., Niemel, J., Steck, J.: A quantum dot neural network. In: Proceedings of the 4th Workshop on Physics and Computation, pp. 22–24 (1996)
19.
Ventura, D., Martinez, T., Smith, G.D., Steele, N.C., Albrecht, R.F.: An artificial neuron with quantum mechanical properties. In: Artificial Neural Nets and Genetic Algorithms, pp. 482–485 (1998)
20.
Matsui, N., Takai, M., Nishimura, H.: A network model based on Qubitlike neuron corresponding to quantum circuit. Electron. Commun. Jpn. 83(10), 67 (2000)CrossRef
21.
Schuld, M., Sinayskiy, I., Petruccione, F.: Quantum walks on graphs representing the firing patterns of a quantum neural network. Phys. Rev. A 89(3), 032333 (2014)ADSCrossRef
22.
Beer, K., Bondarenko, D., Farrelly, T., Osborne, T.J., Salzmann, R., Scheiermann, D., Wolf, R.: Training deep quantum neural networks. Nat. Commun. 11(1), 808 (2020)ADSCrossRefPubMedPubMedCentral
23.
Amin, M.H., Andriyash, E., Rolfe, J., Kulchytskyy, B., Melko, R.: Quantum Boltzmann machine. Phys. Rev. X 8(2), 021050 (2018)
24.
25.
Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nat. Phys. 15(12), 1273 (2019)CrossRef
26.
27.
Romero, J., Olson, J.P., Aspuru-Guzik, A.: Quantum autoencoders for efficient compression of quantum data. Quantum Sci. Technol. 2(4), 045001 (2017)ADSCrossRef
28.
Bondarenko, D., Feldmann, P.: Quantum autoencoders to denoise quantum data. Phys. Rev. Lett. 124(13), 130502 (2020)ADSCrossRefPubMed
29.
Bravo-Prieto, C.: Quantum autoencoders with enhanced data encoding. Mach. Learn. Sci. Technol. 2, 035028 (2021)CrossRef
30.
Pepper, A., Tischler, N., Pryde, G.J.: Experimental realization of a quantum autoencoder: the compression of qutrits via machine learning. Phys. Rev. Lett. 122(6), 060501 (2019)ADSCrossRefPubMed
31.
Huang, C.J., Ma, H., Yin, Q., Tang, J.F., Dong, D., Chen, C., Xiang, G.Y., Li, C.F., Guo, G.C.: Realization of a quantum autoencoder for lossless compression of quantum data. Phys. Rev. A 102(3), 032412 (2020)ADSCrossRef
32.
Locher, D.F., Cardarelli, L., Müller, M.: Quantum error correction with quantum autoencoders. Quantum 7, 942 (2023)CrossRef
33.
Khoshaman, A., Vinci, W., Denis, B., Andriyash, E., Sadeghi, H., Amin, M.H.: Quantum variational autoencoder. Quantum Sci. Technol. 4(1), 014001 (2018)ADSCrossRef
34.
Achache, T., Horesh, L., Smolin, J.: Denoising quantum states with quantum autoencoders - theory and applications, arXiv preprint, arXiv:​2012.​14714, (2020)
35.
Cao, C., Wang, X.: Noise-assisted quantum autoencoder. Phys. Rev. Appl. 15(5), 054012 (2021)ADSCrossRef
36.
Kong, W., Farooq, M.U., Yung, M.H., Guo, G., Wang, X., Zhang, X.M.: Generic detection-based error mitigation using quantum autoencoders. Phys. Rev. A 103(4), L040403 (2021)CrossRef
37.
38.
Tran, Q.H., Kikuchi, S., Oshima, H.: Variational denoising for variational quantum eigensolver, arXiv preprint, arXiv:​2304.​00549, (2023)
39.
Mok, W.K., Zhang, H., Haug, T., Luo, X., Lo, G.Q., Cai, H., Kim, M.S., Liu, A.Q., Kwek, L.C.: Rigorous noise reduction with quantum autoencoders, arXiv preprint, arXiv:​2308.​16153, (2023)
40.
Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
41.
42.
43.
Wang, X., Yu, C.S., Yi, X.X.: An alternative quantum fidelity for mixed states of qudits. Phys. Lett. A 373(1), 58 (2008)ADSCrossRef
44.
Liang, Y.C., Yeh, Y.H., Mendonça, P.E.M.F., Teh, R.Y., Reid, M.D., Drummond, P.D.: Quantum fidelity measures for mixed states. Rep. Prog. Phys. 82(7), 076001 (2019)ADSMathSciNetCrossRefPubMed
Metadata
Title
Denoising quantum mixed states using quantum autoencoders
Author
Ming-Ming Wang
Publication date
01-02-2024
Publisher
Springer US
Published in
Quantum Information Processing / Issue 2/2024
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-023-04239-z

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