AI Powerful Tools in Quantum Finance

  • Raymond S. T. LeeEmail author


This chapter introduces three major artificial intelligence (AI) tools in quantum finance: artificial neural networks (ANNs) on machine learning and time series prediction, fuzzy logics (FLs) on fuzzy and inexact financial modeling and genetic algorithms (GAs) on trading strategy optimization. More importantly, it shows how these AI tools are integrated with quantum finance technology to implement quantum finance forecast and intelligent agent-based program trading systems. First, it starts with a general overview of AI technology. Second, it explores the brain of AI—artificial neural networks (ANNs), which includes biological neural networks, neuron model, classification of ANNs, typical ANNs such as Hopfield network and feedforward backpropagation Network. Third, it explores the optimization engine of AI—genetic algorithms (GAs), which includes general overview of GA, system flow of GA and key operations in GA implementation. After that, it explores the fuzzification engine of AI—fuzzy logics, which includes basic concepts of FL, relationship between FL and uncertainty principle, ICMR fuzzy logic model, applications of fuzzy logic in daily life, fuzzy expert, and fuzzy-neuro systems in quantum finance.


  1. Abraham, T. Rebel (2016) Genius: Warren S. McCulloch’s Transdisciplinary Life in Science. MIT Press, Cambridge.Google Scholar
  2. Black, M. (1937) Vagueness: An Exercise in Logical Analysis. Philosophy of Science 4(4): 427–455.MathSciNetCrossRefGoogle Scholar
  3. Bentivoglio, M. (2014) “Golgi, Camillo”. In Daroff, Robert B.; Aminoff, Michael J. Encyclopedia of the Neurological Sciences (Second edition. ed.). Burlington: Elsevier Science. 464–466.CrossRefGoogle Scholar
  4. Darwin, D. and Huxley, J. (2003) The Origin of Species: 150th Anniversary Edition. Signet; Reprint, Anniversary edition.Google Scholar
  5. Fausett, L. (1994) Fundamentals of Neural Networks Architectures Algorithms and Applications. Prentice Hall.Google Scholar
  6. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional.Google Scholar
  7. Hansen, W. Classical Mythology: A Guide to the Mythical World of the Greeks and Romans. Oxford University Press, 2005.Google Scholar
  8. Hebb, D.O. (1949) The Organization of Behavior. New York: Wiley & Sons.Google Scholar
  9. Holland, J. H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. A Bradford Book; Reprint edition.Google Scholar
  10. Hopfield, J.J. (1984) Neurons with Graded Response Have Collective Computational Properties like Those of Two-State Neurons. Proceedings of the National Academy of Sciences of the United States of America. 81(10): 3088–3092.CrossRefGoogle Scholar
  11. Kramer, O. (2017) Genetic Algorithm Essentials (Studies in Computational Intelligence). Springer.Google Scholar
  12. Lee, R. S. T. Fuzzy-Neuro Approach to Agent Applications (From the AI Perspective to Modern Ontology). Springer-Verlag, Heidelberg Germany, 2006.Google Scholar
  13. Lee, R. S. T. A Transient-chaotic Auto-associative Network (TCAN) based on LEE-oscillators. IEEE Transactions on Neural Networks. 15(5): 1228–1243, 2004.CrossRefGoogle Scholar
  14. McCulloch, W. and Walter, P. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics. 5 (4): 115–133, 1943.MathSciNetCrossRefGoogle Scholar
  15. Patterson, D. W. (1996) Artificial Neural Networks. Prentice Hall.Google Scholar
  16. Ross, T. J. (2016) Fuzzy Logic with Engineering Applications. Wiley.Google Scholar
  17. Russell, S. and Norvig, P. (2015) Artificial Intelligence: A Modern Approach, 3rd edition. Pearson Education India.Google Scholar
  18. Siler, W. and Buckley, J. J. (2004) Fuzzy Expert Systems and Fuzzy Reasoning. Wiley-Interscience.Google Scholar
  19. Silva, et al. (2017) Artificial Neural Networks: A Practical Course. Springer.Google Scholar
  20. Tuchong (2019a) Illustration of AI. Accessed 21 Aug 2019.
  21. Tuchong (2019b) Weak AI. Accessed 21 Aug 2019.
  22. Tuchong (2019c) 3D illustration of integrate-and-fire in neural network. Accessed 21 Aug 2019.
  23. Tuchong (2019d) Neural network and AI. Accessed 21 Aug 2019.
  24. Tuchong (2019e) Populations of chromosomes. Accessed 21 Aug 2019.
  25. Tuchong (2019f) Smart Home with Fuzzy Logic Electronic Home Appliances. Accessed 21 Aug 2019.
  26. Turing, A. (1950) Computing Machinery and Intelligence. Mind, LIX (236): 433–460.MathSciNetCrossRefGoogle Scholar
  27. Zadeh, L. A. and Aliev, R. A. (2019) Fuzzy Logic Theory and Applications: Part I and Part II. World Scientific Publishing Company.Google Scholar
  28. Zadeh, L. A. (1975a) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning-I. Information Sciences. 8(1): 199–249.MathSciNetCrossRefGoogle Scholar
  29. Zadeh, L. A. (1975b) The Concept of a Linguistic Variable and its Application to Approximate Reasoning-II. Information Sciences. 8(4): 301–357.MathSciNetCrossRefGoogle Scholar
  30. Zadeh, L. A. (1975c) The Concept of a Linguistic Variable and its Application to Approximate Reasoning-III. Information Sciences. 9(1): 43–80.MathSciNetCrossRefGoogle Scholar
  31. Zadeh, L. A. (1965) Fuzzy sets. Information and Control. 8 (3): 338–353.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Division of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International College (UIC)ZhuhaiChina

Personalised recommendations