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Related Experiment Video

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Best practices for portfolio optimization by quantum computing, experimented on real quantum devices.

Giuseppe Buonaiuto1, Francesco Gargiulo1, Giuseppe De Pietro1

  • 1Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), 80131, Naples, Italy.

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Summary
This summary is machine-generated.

This study explores quantum computing for portfolio optimization, finding that the Variational Quantum Eigensolver (VQE) can achieve near-exact solutions with proper hyperparameter tuning on quantum hardware. The research identifies optimal settings for VQE to enhance efficiency in financial portfolio management.

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Area of Science:

  • Quantum Computing
  • Computational Finance
  • Optimization Algorithms

Background:

  • Portfolio optimization faces scalability challenges with increasing market dimensions.
  • Quantum computing offers a potential solution to overcome computational complexity in finance.
  • Classical optimization methods struggle with large-scale, constrained quadratic problems.

Purpose of the Study:

  • To solve the portfolio optimization problem using the Variational Quantum Eigensolver (VQE).
  • To identify and define the optimal hyperparameters for VQE in portfolio optimization on real quantum computers.
  • To assess the performance and scalability of VQE for financial applications.

Main Methods:

  • Formulating the constrained quadratic portfolio optimization problem.
  • Translating the problem into Quadratic Unconstrained Binary Optimization (QUBO) using binary encoding.
  • Converting the QUBO problem into an Ising Hamiltonian for quantum computation.
  • Employing the Variational Quantum Eigensolver (VQE) to find the minimum eigenvalue (optimal solution).
  • Experimenting with various ansatzes and optimization methods on simulators and real quantum devices.

Main Results:

  • VQE performance is highly dependent on quantum hardware size and hyperparameter selection.
  • Optimal hyperparameter choices enable VQE on real devices to reach solutions close to exact ones.
  • The quantum algorithm demonstrates strong convergence towards classical solutions, even without error mitigation.
  • Solution quality correlates with quantum processor dimension, as shown on different quantum devices.
  • The study provides evidence for the best practices for VQE-based portfolio optimization.

Conclusions:

  • The Variational Quantum Eigensolver (VQE) is a viable and efficient method for portfolio optimization on quantum computers.
  • Careful selection of hyperparameters and sufficient quantum hardware are crucial for achieving high-quality results.
  • Quantum computing, particularly VQE, holds promise for more efficient financial optimization as hardware scales.