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Use Cases of Quantum Optimization for Finance

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arxiv 2010.01312 v1 pith:EVD66QE4 submitted 2020-10-03 q-fin.GN

Samuel Mugel , Enrique Lizaso , Roman Orus This is my paper · ORCID
classification q-fin.GN
keywords quantumoptimizationfinancealgorithmsannealersappliedbrieflycarry
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In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Boosting Binomial Exotic Option Pricing with Tensor Networks

    q-fin.CP 2025-05 conditional novelty 6.0 of 10

    Tensor-network approximations price binomial Asian and multi-asset American basket options with linear-in-size cost in tested regimes, beating Monte Carlo for high volatility and small time steps.

  2. Quantum Entrepreneurship Lab: Training a Future Workforce for the Quantum Industry

    physics.ed-ph 2025-05 unverdicted novelty 5.0 of 10

    The Quantum Entrepreneurship Lab at TUM is a project-based course pairing technical and business students to work on quantum research topics and craft startup pitches.

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