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Production and Targeting of Monovalent Quantum Dots
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Multiclass portfolio optimization via variational quantum Eigensolver with Dicke state ansatz.

J V S Scursulim1, Gabriel M Langeloh2, Victor L Beltran2

  • 1Instituto de Ciência e Tecnologia Itaú, São Paulo, Brazil. jose.scursulim@itau-unibanco.com.br.

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|February 13, 2026
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Summary
This summary is machine-generated.

This study introduces a novel quantum framework for diversification-aware portfolio optimization. The Dicke state ansatz significantly enhances performance by ensuring feasible solutions, outperforming existing methods.

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Area of Science:

  • Quantum Computing
  • Computational Finance
  • Optimization Algorithms

Background:

  • Portfolio optimization is crucial in finance but often neglects diversification.
  • Existing quantum algorithms for portfolio optimization lack explicit diversification constraints.
  • Realistic financial models require incorporating diversification for practical application.

Purpose of the Study:

  • To develop a novel quantum framework for multiclass portfolio optimization that explicitly incorporates diversification.
  • To introduce a new ansatz for the Variational Quantum Eigensolver (VQE) that inherently satisfies diversification constraints.
  • To analyze the impact of classical optimizers on the performance of this hybrid quantum-classical approach.

Main Methods:

  • Utilized multiple parametrized Dicke states as a VQE ansatz to encode diversification constraints.
  • Initialized the quantum system in a superposition of feasible states, reducing the search space and eliminating the need for penalty terms.
  • Evaluated the performance with different classical optimizers, focusing on the CMA-ES optimizer.

Main Results:

  • The Dicke state ansatz successfully encoded diversification constraints, ensuring only feasible portfolio states were explored.
  • The hybrid quantum-classical approach, particularly with the CMA-ES optimizer, demonstrated superior convergence rate, approximation ratio, and measurement probability.
  • The method significantly reduced the computational search space by avoiding penalty terms.

Conclusions:

  • The proposed quantum framework offers a promising solution for practical, diversification-aware portfolio optimization.
  • The Dicke state ansatz is a key innovation for efficiently handling constraints in quantum optimization.
  • This approach has significant potential for application in the financial sector, addressing real-world portfolio management challenges.