Skip to main content
SpringerLink
Log in
Book cover

Science and Information Conference

SAI 2022: Intelligent Computing pp 394–403Cite as

The Machine Learning Principles Based at the Quantum Mechanics Postulates

  • Conference paper
  • First Online:

  • 731 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 506))

Abstract

Quantum mechanics is governed by well-defined postulates by the which one can go through either theory or experimental studies in order to perform measurements of microscopic dynamics of elementary particles, atoms and molecules for instance. By knowing the Tom Mitchell criteria inside Machine Learning, then one can wonder about the postulates of Quantum Mechanics in the entire picture of Mitchell criteria. This paper tries to answer this question. In essence it is focused on the role of brackets formalism and how it makes more feasible to project the ground principles of Quantum Mechanics in the arena of Machine Learning and Artificial Intelligence.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Modern Quantum Mechanics by Napolitano Jim, Sakurai J.J, 2017, Chapter-II

    Google Scholar 

  2. Mechanics, Q., Cohen-Tannoudji, C., Diu, B., Laloe, F.: Quantum Mechanics. Wiley-VCH Verlag GmbH. Vol-I, Chapter-II (2019)

    Google Scholar 

  3. Feynman, R.P.: The concept of probability in quantum mechanics. In: Proceedings of the Second Berkeley Symposium on Mathematical Statistics (1951)

    Google Scholar 

  4. Feynman, R.P.: Space-time approach to quantum electrodynamics. Phys. Rev. 76(6), 769 (1949)

    Article  MathSciNet  Google Scholar 

  5. Feynman, R.P.: Space-time approach to non-relativistic quantum mechanics. Rev. Mod. Phys. 20(2), 367 (1948)

    Google Scholar 

  6. Tanaka, T., Mitchell, T.M.: Embedding learning in a general frame-based architecture. Int. J Patt. R Artif. Intell. 4(2), 125–145 (1990)

    Article  Google Scholar 

  7. Mitchell, T.M., Steinberg, L.I., Shulman, J.S.: A knowledge-based approach to design. IEEE Trans. Patt. Anal. Mach. Intell. PAMI–7(5), 502–510 (1985). https://doi.org/10.1109/TPAMI.1985.4767698

    Article  Google Scholar 

  8. Mahadevan, S., Mitchell, T., Mostow, D.J., Steinberg, L., Tadepalli, P.: An apprentice-based approach to knowledge acquisition. Artif. Intell. 64(1), 1–52 (1993)

    Article  Google Scholar 

  9. Nieto-Chaupis, H.: Theory of machine learning based on nonrelativistic quantum mechanics. Int. J. Quant. Inform. (2021). https://doi.org/10.1142/S0219749921410045

    Article  MathSciNet  MATH  Google Scholar 

  10. Biehl, M., Opper, M.: Tiling like learning in the parity machine. Phys. Rev. A 44, 6888 (1991)

    Google Scholar 

  11. Saad, D., Solla, S.A.: On-line learning in soft committee machines. Phys. Rev. E 52, 4225 (1995)

    Google Scholar 

  12. Tanaka, T.: Mean-field theory of Boltzmann machine learning. Phys. Rev. E 58, 2302 (1998)

    Google Scholar 

  13. Rosen-Zvi, M., Klein, E., Kanter, I., Kinzel, W.: Mutual learning in a tree parity machine and its application to cryptography. Phys. Rev. E 66, 066135 (2002)

    Google Scholar 

  14. Lutz, R.: Learning about one way of learning. Nature 325, 118–118 (1987)

    Google Scholar 

  15. Gonzalez-Henao, J.C., Pugliese, E., Euzzor, S., Meucci, R., Roversi, J.A., Arecchi, F.T.: Control of entanglement dynamics in a system of three coupled quantum oscillators. Sci. Rep. 7, 9957 (2017)

    Google Scholar 

  16. Nakanishi, N.: Multiple poles in the scattering Green’s function. Phys. Rev. 140, B947 (1965)

    Google Scholar 

  17. Hara, S., Ono, T., Okamoto, R., Washio, T., Takeuchi, S.: Quantum-state anomaly detection for arbitrary errors using a machine-learning technique. Phys. Rev. A 94, 042341 (2016)

    Google Scholar 

  18. Bachtis, D., Aarts, G., Lucini, B.: Quantum field-theoretic machine learning. Phys. Rev. D 103, 074510 (2021)

    Google Scholar 

  19. Liu, Z., Tegmark, M.: Machine learning conservation laws from trajectories. Phys. Rev. Lett. 126, 180604 (2021)

    Google Scholar 

  20. Wolfram. www.wolfram.com

Download references

Author information

Authors and Affiliations

  1. Universidad Autónoma del Perú, Panamericana Sur Km 16.3 Villa el Salvador Lima, Lima, Peru

    Huber Nieto-Chaupis

Authors
  1. Huber Nieto-Chaupis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huber Nieto-Chaupis .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nieto-Chaupis, H. (2022). The Machine Learning Principles Based at the Quantum Mechanics Postulates. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_27

Download citation

Publish with us

Policies and ethics

Search

Navigation