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Quantum Finance: a tutorial on quantum computing applied to the financial market

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Listed:
  • Askery Canabarro
  • Taysa M. Mendonc{c}a
  • Ranieri Nery
  • George Moreno
  • Anton S. Albino
  • Gleydson F. de Jesus
  • Rafael Chaves

Abstract

Previously only considered a frontier area of Physics, nowadays quantum computing is one of the fastest growing research field, precisely because of its technological applications in optimization problems, machine learning, information security and simulations. The goal of this article is to introduce the fundamentals of quantum computing, focusing on a promising quantum algorithm and its application to a financial market problem. More specifically, we discuss the portfolio optimization problem using the \textit{Quantum Approximate Optimization Algorithm} (QAOA). We not only describe the main concepts involved but also consider simple practical examples, involving financial assets available on the Brazilian stock exchange, with codes, both classic and quantum, freely available as a Jupyter Notebook. We also analyze in details the quality of the combinatorial portfolio optimization solutions through QAOA using SENAI/CIMATEC's ATOS QLM quantum simulator.

Suggested Citation

  • Askery Canabarro & Taysa M. Mendonc{c}a & Ranieri Nery & George Moreno & Anton S. Albino & Gleydson F. de Jesus & Rafael Chaves, 2022. "Quantum Finance: a tutorial on quantum computing applied to the financial market," Papers 2208.04382, arXiv.org, revised Aug 2022.
    • Handle: RePEc:arx:papers:2208.04382
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    Cited by:

    1. Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.

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