Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Quantum Information Processing
Erschienen in: Quantum Information Processing 5/2023

01.05.2023

Quantity study on a novel quantum neural network with alternately controlled gates for binary image classification

verfasst von: Qi Bai, Xianliang Hu

Erschienen in: Quantum Information Processing | Ausgabe 5/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

A novel quantum neural network(QNN) is proposed, in which quantum probability image encoding(QPIE) and specially designed ansatz are used. QPIE can exponentially reduce qubits for image encoding by using quantum superposition. The parameter gates in ansatz are selected from the universal gate set for quantum computing, which guarantees the expressibility of models. The proposed QNN can be trained by supervised learning. In this article, various experiments are conducted to explore the factors that affect accuracy. The results derive from MNIST show that both the improvement of resolution and the repetition of layers have a positive contribution to accuracy. The enhancement of the expressibility of a single layer by replacing CX gates with \(\hbox {R}_y\) gates also improves the performance of the model.
Vorheriger Artikel A d-level quantum secret sharing scheme with cheat-detection (t, m) threshold
Nächster Artikel A family of bipartite separability criteria based on Bloch representation of density matrices

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41(2), 303–332 (1999)MathSciNetCrossRefMATHADS
2.
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)
3.
Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)MathSciNetCrossRefADS
4.
5.
6.
Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10(9), 631–633 (2014)CrossRef
7.
Rebentrost, P., Mohseni, M., Lloyd, S.: Quantum support vector machine for big data classification. Phys. Rev. Lett. 113(13), 130503 (2014)CrossRefADS
8.
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)CrossRef
9.
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., Gambetta, J.M.: Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549(7671), 242–246 (2017)CrossRefADS
10.
Grimsley, H.R., Economou, S.E., Barnes, E., Mayhall, N.J.: An adaptive variational algorithm for exact molecular simulations on a quantum computer. Nat. Commun. 10(1), 1–9 (2019)CrossRef
11.
Menneer, T., Narayanan, A.: Quantum-inspired neural networks. Technical Report R329, University of Exeter, Exeter (1995).
12.
13.
Li, Z., Liu, X., Xu, N., Du, J.: Experimental realization of a quantum support vector machine. Phys. Rev. Lett. 114(14), 140504 (2015)CrossRefADS
14.
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)CrossRef
15.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)CrossRefMATHADS
16.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
17.
18.
19.
Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nat. Phys. 15(12), 1273–1278 (2019)CrossRef
20.
Schuld, M., Bocharov, A., Svore, K.M., Wiebe, N.: Circuit-centric quantum classifiers. Phys. Rev. A 101(3), 032308 (2020)MathSciNetCrossRefADS
21.
Zeng, Y., Wang, H., He, J., Huang, Q., Chang, S.: A multi-classification hybrid quantum neural network using an all-qubit multi-observable measurement strategy. Entropy 24(3), 394 (2022)CrossRefADS
22.
Zhao, W., Wang, Y., Qu, Y., Ma, H., Wang, S.: Binary classification quantum neural network model based on optimized grover algorithm. Entropy 24(12), 1783 (2022)MathSciNetCrossRefADS
23.
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), 1–8 (2018)
24.
Oh, S., Choi, J., Kim, J.: A tutorial on quantum convolutional neural networks (qcnn). In: 2020 International Conference on Information and Communication Technology Convergence (ICTC), pp. 236–239 (2020)
25.
Li, W., Chu, P.-C., Liu, G.-Z., Tian, Y.-B., Qiu, T.-H., Wang, S.-M.: An image classification algorithm based on hybrid quantum classical convolutional neural network. Quantum Eng. 2022 (2022)
26.
Venegas-Andraca, S.E., Bose, S.: Storing, processing and retrieving an image using quantum mechanics. In: Proceedings of the SPIE Conference Quantum Information and Computation, pp. 137–147 (2003)
27.
28.
Le, P.Q., Dong, F., Hirota, K.: A flexible representation of quantum images for polynomial preparation, image compression, and processing operations. Quant. Inf. Process. 10(1), 63–84 (2011)MathSciNetCrossRefMATH
29.
Zhang, Y., Lu, K., Gao, Y., Wang, M.: Neqr: a novel enhanced quantum representation of digital images. Quant. Inf. Process. 12(8), 2833–2860 (2013)MathSciNetCrossRefMATHADS
30.
Yao, X.-W., Wang, H., Liao, Z., Chen, M.-C., Pan, J., Li, J., Zhang, K., Lin, X., Wang, Z., Luo, Z.: Quantum image processing and its application to edge detection: theory and experiment. Phys. Rev. X 7(3), 031041 (2017)
31.
Nielsen, M.A., Chuang, I.: Quantum Computation and Quantum Information. American Association of Physics Teachers, College Park (2002)MATH
32.
Kitaev, A.Y., Shen, A., Vyalyi, M.N., Vyalyi, M.N.: Classical and Quantum Computation, vol. 47. American Mathematical Society, Providence (2002)MATH
33.
34.
Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4(4), 043001 (2019)CrossRefADS
35.
36.
Shi, J., Tang, Y., Lu, Y., Feng, Y., Shi, R., Zhang, S.: IEEE Transactions on Pattern Analysis and Machine Intelligence. Quantum circuit learning with parameterized boson sampling, IEEE, Data Eng (2021)
37.
Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quantum Technol. 2(12), 1900070 (2019)CrossRef
38.
Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K.: Quantum circuit learning. Phys. Rev. A 98(3), 032309 (2018)CrossRefADS
39.
Schuld, M., Bergholm, V., Gogolin, C., Izaac, J., Killoran, N.: Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99(3), 032331 (2019)CrossRefADS
40.
Broughton, M., Verdon, G., McCourt, T., Martinez, A.J., Yoo, J.H., Isakov, S.V., Massey, P., Halavati, R., Niu, M.Y., Zlokapa, A., et al.: Tensorflow quantum: A software framework for quantum machine learning (2020) arXiv:​2003.​02989
Metadaten
Titel
Quantity study on a novel quantum neural network with alternately controlled gates for binary image classification
verfasst von
Qi Bai
Xianliang Hu
Publikationsdatum
01.05.2023
Verlag
Springer US
Erschienen in
Quantum Information Processing / Ausgabe 5/2023
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-023-03929-y

Weitere Artikel der Ausgabe 5/2023

Quantum Information Processing 5/2023 Zur Ausgabe

OriginalPaper

How to verify identity in the continuous variable quantum system?

OriginalPaper

A new definition of hitting time and an embedded Markov chain in continuous-time quantum walks

OriginalPaper

Quantum illumination with classical correlated light

OriginalPaper

One-way deficit and Holevo quantity of generalized n-qubit Werner state

OriginalPaper

One-particle loss detection of genuine multipartite entanglement

OriginalPaper

Evaluating the convergence of tabu enhanced hybrid quantum optimization