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We developed a Generator-Enhanced Optimization (GEO) strategy using quantum-inspired tensor-network Born machines. This approach excels in portfolio optimization, demonstrating practical value and a promising step toward quantum advantage.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Financial Optimization

Background:

  • Combinatorial optimization algorithms face challenges in efficient search space exploration.
  • Generative models offer a novel approach to solving complex optimization problems.

Purpose of the Study:

  • Introduce the Generator-Enhanced Optimization (GEO) strategy, a flexible framework for optimization.
  • Focus on a quantum-inspired GEO using tensor-network Born machines (TN-GEO).
  • Evaluate TN-GEO's performance on the cardinality-constrained portfolio optimization problem.

Main Methods:

  • Developed the TN-GEO framework leveraging tensor-network Born machines.
  • Constructed portfolio optimization problem instances from S&P 500 and other financial stock indexes.
  • Benchmarked TN-GEO against state-of-the-art optimization algorithms.

Main Results:

  • TN-GEO demonstrates significant value in an industrial application (portfolio optimization).
  • The quantum-inspired generative models exhibit strong generalization capabilities.
  • TN-GEO achieves competitive performance, rivaling highly-tuned, decades-old algorithms.

Conclusions:

  • TN-GEO represents a powerful new strategy for combinatorial optimization.
  • Quantum-inspired models show promise for practical advantage in real-world problems.
  • This work paves the way for future applications of quantum generative models.