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Particle swarm optimization for a variational quantum eigensolver.

Hao Mei1,2, Jianyu Zhao3, Qing-Song Li4

  • 1Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230088, China. qingchun720@ustc.edu.cn.

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Summary
This summary is machine-generated.

A new optimization algorithm, gradient-based adaptive quantum-behaved particle swarm optimization (GAQPSO), improves variational quantum eigensolver (VQE) performance. GAQPSO enhances noise resistance and accuracy for quantum chemistry problems.

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Quantum Algorithms

Background:

  • Variational quantum eigensolver (VQE) is crucial for finding molecular ground and excited states.
  • VQE performance is limited by classical optimization and quantum device noise.

Purpose of the Study:

  • To introduce and evaluate a novel optimization algorithm for VQE.
  • To enhance VQE's robustness against noise and improve optimization accuracy.

Main Methods:

  • Particle Swarm Optimization (PSO) was adapted for VQE.
  • A modified PSO, Gradient-based Adaptive Quantum-behaved Particle Swarm Optimization (GAQPSO), was developed.
  • GAQPSO utilizes gradient information and swarm intelligence for adaptive parameter updates.

Main Results:

  • GAQPSO demonstrated superior accuracy and noise resilience in VQE simulations compared to traditional PSO, QPSO, COBYLA, GD, and L-BFGS-B.
  • The algorithm achieved accurate results even with random initial values and in noisy quantum environments.
  • Simulations were performed on various molecular systems with different geometries.

Conclusions:

  • GAQPSO offers a significant advancement in optimizing VQE for quantum chemistry.
  • This novel approach enhances VQE's practical applicability and moves closer to achieving quantum advantage.
  • GAQPSO shows promise for overcoming key challenges in near-term quantum computing applications.