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Quantum computing reduces systemic risk in financial networks.

Amine Mohamed Aboussalah1, Cheng Chi2, Chi-Guhn Lee2

  • 1Department of Financial and Risk Engineering, New York University, New York, USA. ama10288@nyu.edu.

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Summary
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This study optimizes financial networks to mitigate systemic risk. A novel two-stage quantum algorithm reduces bank failures and enhances resilience against financial shocks.

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Area of Science:

  • Financial network analysis
  • Systemic risk mitigation
  • Quantum computing applications

Background:

  • Highly connected financial networks are vulnerable to cascading failures.
  • Systemic risk arises from interdependencies, necessitating mitigation strategies.
  • Previous models lacked realistic nonlinear losses and scalability.

Purpose of the Study:

  • To develop and evaluate a novel two-stage algorithm for optimizing financial network connections.
  • To mitigate systemic risk by preventing cascading bank failures.
  • To incorporate nonlinear losses for a more realistic simulation environment.

Main Methods:

  • Developed a two-stage algorithm for network partitioning and module optimization.
  • Implemented new classical and quantum partitioning algorithms for directed, weighted graphs.
  • Applied a new methodology for Mixed Integer Linear Programming with systemic risk constraints.
  • Compared classical and quantum partitioning algorithm performance.

Main Results:

  • The two-stage optimization with quantum partitioning demonstrated increased resilience to financial shocks.
  • Quantum partitioning delayed the cascade failure phase transition.
  • Reduced total bank failures at convergence under systemic risks.
  • Achieved reduced time complexity compared to classical methods.

Conclusions:

  • The proposed quantum-enhanced two-stage optimization effectively mitigates systemic risk in financial networks.
  • Quantum partitioning offers significant advantages in network resilience and failure reduction.
  • The methodology provides a scalable and efficient approach to managing financial network stability.