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Gradient Echo Quantum Memory in Warm Atomic Vapor
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Using Differential Evolution to avoid local minima in Variational Quantum Algorithms.

Daniel Faílde1, José Daniel Viqueira2,3, Mariamo Mussa Juane2

  • 1Centro de Supercomputación de Galicia (CESGA), 15705, Santiago de Compostela, Spain. dfailde@cesga.es.

Scientific Reports
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

Differential Evolution (DE) offers a gradient-free optimization method for Variational Quantum Algorithms (VQAs), overcoming local minima and barren plateaus. DE significantly improves success rates in quantum simulations compared to traditional optimizers.

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Area of Science:

  • Quantum Computing
  • Computational Optimization

Background:

  • Variational Quantum Algorithms (VQAs) are promising for NISQ-era quantum computing.
  • VQAs face challenges with local minima and barren plateaus, hindering scalability.
  • Gradient-dependent optimizers struggle with these VQA optimization issues.

Purpose of the Study:

  • To investigate alternative optimization methods for VQAs.
  • To apply the Differential Evolution (DE) algorithm to VQA optimization.
  • To assess DE's resilience to local minima and vanishing gradients.

Main Methods:

  • Applied Differential Evolution (DE) to Variational Quantum Eigensolver (VQE) optimization.
  • Compared DE against state-of-the-art local optimizers (SLSQP, COBYLA, L-BFGS-B, SPSA).
  • Evaluated performance on a 1D Ising chain and a 1D Hubbard model.

Main Results:

  • DE consistently outperformed local optimizers in VQA optimization.
  • DE achieved 100% success rate for ground state in a 14-qubit 1D Ising chain, versus ~40% for local optimizers.
  • Combining DE with local optimizers enhanced energy estimation accuracy.
  • DE demonstrated effectiveness on more complex problems like the 1D Hubbard model.

Conclusions:

  • Differential Evolution is a robust and effective optimizer for Variational Quantum Algorithms.
  • DE overcomes critical limitations like local minima and barren plateaus in VQAs.
  • DE presents a viable strategy for advancing quantum computing applications.