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Quantum Information Processing

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01-03-2024

Parallelized variational quantum classifier with shallow QRAM circuit

Authors: Bojia Duan, Xin Sun, Chang-Yu Hsieh

Published in: Quantum Information Processing | Issue 3/2024

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Abstract

In this paper, we present a novel quantum variational circuit, harnessing the capabilities of a pre-determined quantum random access memory (QRAM) circuit to enhance machine learning tasks. Our approach enables parallel training on entire datasets, facilitated by QRAM’s streamlined data loading onto qubits through probability amplitudes. This leads to a logarithmic reduction in the qubit width (number of qubits) concerning data dimension and dataset size. Our model is adaptable to various loss functions, rendering it suitable for binary and multi-class classification tasks. To validate our methodology, we conducted numerical simulations using well-established benchmark datasets, Iris and handwritten digits, with the Pennylane platform. Impressively, our approach yielded high classification accuracy in these illustrations. This work demonstrates promising potential for quantum machine learning applications, especially when dealing with large datasets.

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Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195–202 (2017)ADSCrossRef

Ciliberto, C., Herbster, M., Ialongo, A.D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L.: Quantum machine learning: a classical perspective. Proc. Royal Soc. A Math. Phys. Eng. Sci. 474(2209), 20170551 (2018)ADSMathSciNet

Wiebe, N., Braun, D., Lloyd, S.: Quantum algorithm for data fitting. Physical Rev. Lett. 109(5), 050505 (2012)ADSCrossRef

Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big feature and big data classification. Physical Rev. Lett. 113(13), 130503 (2013)CrossRef

Duan, B., Yuan, J., Liu, Y., Li, D.: Quantum algorithm for support matrix machines. Physical Rev. A 96(3), 032301 (2017)ADSCrossRef

Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 108–1131 (2014)CrossRef

Kerenidis, I., Prakash, A.: Quantum Recommendation Systems. In: 8th Innovations in Theoretical Computer Science Conference (ITCS 2017). Leibniz International Proceedings in Informatics (LIPIcs), 67, 49–14921 (2017)

Duan, B., Yuan, J., Yu, C.-H., Huang, J., Hsieh, C.-Y.: A survey on hhl algorithm: From theory to application in quantum machine learning. Phys. Lett. A 384(24), 126595 (2020)MathSciNetCrossRef

Preskill, J.: Quantum computing in the nisq era and beyond. Quantum 2, 79 (2018)CrossRef

10.

Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quant. Sci. Technol. 4(4), 043001 (2019)ADSCrossRef

11.

Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Code/Software sharing not applicable to this article as nocode/software was generated or analysed during the current study.Circuit-centric quantum classifiers. Physical Rev. A 101(3), 032308 (2020)

12.

Pérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., Latorre, J.I.: Data re-uploading for a universal quantum classifier. Quantum 4, 226 (2020)CrossRef

13.

Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V., Green, A.G., Severini, S.: Hierarchical quantum classifiers. npj Quant. Inf. 4(1), 65 (2018)ADSCrossRef

14.

LaRose, R., Coyle, B.: Robust data encodings for quantum classifiers. Physical Rev. A 102(3), 032420 (2020)ADSCrossRef

15.

Abbas, A., Schuld, M., Petruccione, F.: On quantum ensembles of quantum classifiers. Quant. Mach. Intell. 2, 1–8 (2020)

16.

Adhikary, S.: Entanglement assisted training algorithm for supervised quantum classifiers. Quant. Inf. Proc. 20(8), 254 (2021)MathSciNetCrossRef

17.

Fan, L., Situ, H.: Compact data encoding for data re-uploading quantum classifier. Quant. Inf. Proc. 21(3), 87 (2022)MathSciNetCrossRef

18.

Bai, Q., Hu, X.: Quantity study on a novel quantum neural network with alternately controlled gates for binary image classification. Quant. Inf. Proc. 22(5), 184 (2023)MathSciNetCrossRef

19.

Sun, X., Tian, G., Yang, S., Yuan, P., Zhang, S.: Asymptotically optimal circuit depth for quantum state preparation and general unitary synthesis. arXiv, preprint arXiv:2108.06150 (2021)

20.

Zhang, X.-M., Yung, M.-H., Yuan, X.: Low-depth quantum state preparation. Physical Rev. Res. 3(4), 043200 (2021)ADSCrossRef

21.

Nakaji, K., Uno, S., Suzuki, Y., Raymond, R., Onodera, T., Tanaka, T., Tezuka, H., Mitsuda, N., Yamamoto, N.: Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicators. Physical Rev. Res. 4(2), 023136 (2022)ADSCrossRef

22.

Möttönen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.: Transformation of quantum states using uniformly controlled rotations. Quant. Inf. Comput. 5 (2005)

23.

Zhang, X.-M., Li, T., Yuan, X.: Quantum state preparation with optimal circuit depth: Implementations and applications. arXiv preprint arXiv:2201.11495 (2022)

24.

Johri, S., Debnath, S., Mocherla, A., Singk, A., Prakash, A., Kim, J., Kerenidis, I.: Nearest centroid classification on a trapped ion quantum computer. npj Quant. Inf. 7(1), 1–11 (2021)

25.

Araujo, I.F., Park, D.K., Petruccione, F., Silva, A.J.: A divide-and-conquer algorithm for quantum state preparation. Scientific Rep. 11(1), 1–12 (2021)

26.

Shirakawa, T., Ueda, H., Yunoki, S.: Automatic quantum circuit encoding of a given arbitrary quantum state. arXiv preprint arXiv:2112.14524 (2021)

27.

Marin-Sanchez, G., Gonzalez-Conde, J., Sanz, M.: Quantum algorithms for approximate function loading. arXiv preprint arXiv:2111.07933 (2021)

28.

Romero, J., Aspuru-Guzik, A.: Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions. Adv. Quant. Technol. 4(1), 2000003 (2021)CrossRef

29.

Zoufal, C., Lucchi, A., Woerner, S.: Quantum generative adversarial networks for learning and loading random distributions. npj Quant. Inf. 5(1), 1–9 (2019)

30.

Gomez, A.M., Yelin, S.F., Najafi, K.: Reconstructing quantum states using basis-enhanced born machines. arXiv preprint arXiv:2206.01273 (2022)

31.

Giovannetti, V., Lloyd, S., Maccone, L.: Quantum random access memory. Physical Rev. Lett. 100(16), 160501 (2008)ADSMathSciNetCrossRef

32.

Giovannetti, V., Lloyd, S., Maccone, L.: Architectures for a quantum random access memory. Physical Rev. A 78(5), 052310 (2008)ADSCrossRef

33.

Hong, F.-Y., Xiang, Y., Zhu, Z.-Y., Jiang, L.-z., Wu, L.-n.: Robust quantum random access memory. Physical Rev. A 86(1), 010306 (2012)

34.

Hann, C.T., Zou, C.-L., Zhang, Y., Chu, Y., Schoelkopf, R.J., Girvin, S.M., Jiang, L.: Hardware-efficient quantum random access memory with hybrid quantum acoustic systems. Physical Rev. Lett. 123(25), 250501 (2019)ADSCrossRef

35.

Sun, X., Tian, G., Yang, S., Yuan, P., Zhang, S.: Asymptotically optimal circuit depth for quantum state preparation and general unitary synthesis. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2023)

36.

Zhang, X.-M., Li, T., Yuan, X.: Quantum state preparation with optimal circuit depth: Implementations and applications. Physical Rev. Lett. 129(23), 230504 (2022)ADSMathSciNetCrossRef

37.

Niu, M.Y., Zlokapa, A., Broughton, M., Boixo, S., Mohseni, M., Smelyanskyi, V., Neven, H.: Entangling quantum generative adversarial networks. Physical Rev. Lett. 128(22), 220505 (2022)ADSMathSciNetCrossRef

38.

Phalak, K., Li, J., Ghosh, S.: Trainable pqc-based qram for quantum storage. IEEE Access (2023)

39.

Duan, B., Hsieh, C.-Y.: Hamiltonian-based data loading with shallow quantum circuits. Physical Rev. A 106(5), 052422 (2022)ADSMathSciNetCrossRef

40.

Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P.J., Aspuru-Guzik, A., O’brien, J.L.: A variational eigenvalue solver on a photonic quantum processor. Nat. Commun. 5(1), 1–7 (2014)

41.

Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Alam, M.S., Ahmed, S., Arrazola, J.M., Blank, C., Delgado, A., Jahangiri, S., et al.: Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968 (2018)

42.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

43.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Title: Parallelized variational quantum classifier with shallow QRAM circuit
Authors: Bojia Duan
Xin Sun
Chang-Yu Hsieh
Publication date: 01-03-2024
Publisher: Springer US
Published in: Quantum Information Processing / Issue 3/2024
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI: https://doi.org/10.1007/s11128-024-04295-z

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