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Related Experiment Videos

Large-scale portfolio optimization using Pauli correlation encoding.

Vicente P Soloviev1, Michal Krompiec2

  • 1Fujitsu Research of Europe Ltd., Madrid, Spain. vicente.perezsoloviev@fujitsu.com.

Scientific Reports
|June 2, 2026
PubMed
Summary

This study introduces a novel quantum algorithm for portfolio optimization, enabling efficient risk-return balancing for large financial datasets. The quantum approach enhances scalability for real-world applications.

Keywords:
Pauli correlation encodingPortfolio optimizationVariational algorithm

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Area of Science:

  • Quantum Computing
  • Computational Finance
  • Optimization Algorithms

Background:

  • Traditional portfolio optimization relies on classical algorithms, facing limitations in high-dimensional spaces.
  • Quantum computing offers potential for efficient exploration of complex financial solution spaces.
  • Current quantum methods are limited by hardware constraints, restricting qubit-to-variable ratios.

Purpose of the Study:

  • To apply a gate-based variational quantum algorithm to a real-world portfolio optimization problem.
  • To overcome hardware limitations by assigning multiple variables per qubit.
  • To demonstrate improved scalability for quantum-enhanced financial applications.

Main Methods:

  • Utilized the Pauli Correlation Encoding algorithm for a gate-based variational quantum approach.
  • Applied the algorithm to a portfolio optimization problem with over 250 variables.
  • Iteratively partitioned a real stock market graph into sub-portfolios of correlated assets.
  • Incorporated a classical postprocessing step for final portfolio reduction and optimization.

Main Results:

  • Successfully applied a multi-variable-per-qubit quantum algorithm to a large-scale financial problem.
  • Demonstrated improved scalability compared to traditional variational quantum methods.
  • Enabled the optimization of portfolios with over 250 variables using quantum computation.

Conclusions:

  • The Pauli Correlation Encoding algorithm effectively addresses scalability limitations in gate-based quantum portfolio optimization.
  • This approach enhances the feasibility of quantum computing for real-world financial applications.
  • Opens new avenues for quantum-enhanced financial decision-making and risk management.