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We developed a new quantum algorithm optimizer called Sequential Optimization with Approximate Parabola (SOAP) to improve the efficiency of the Variational Quantum Eigensolver (VQE). SOAP significantly reduces the number of measurements needed for parameter optimization in quantum chemistry simulations.

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Algorithm Optimization

Background:

  • The Variational Quantum Eigensolver (VQE) is a key algorithm for near-term quantum computers.
  • Parameter optimization is a major challenge in VQE, demanding extensive measurements.
  • Efficient optimization is crucial for advancing quantum algorithms.

Purpose of the Study:

  • To introduce Sequential Optimization with Approximate Parabola (SOAP) as a novel optimizer for VQE.
  • To enhance the efficiency and robustness of parameter optimization for quantum algorithms.
  • To reduce the number of energy evaluations required in VQE.

Main Methods:

  • SOAP utilizes sequential optimization and approximates the energy landscape with quadratic functions.
  • It incorporates average directions from previous iterations to capture parameter correlations.
  • The method is designed for optimizing the unitary coupled-cluster ansatz on quantum computers.

Main Results:

  • SOAP demonstrates faster convergence and improved robustness to noise compared to traditional optimizers.
  • Numerical benchmarks on molecular systems show significant performance gains.
  • Simulations up to 20 qubits indicate good scalability with the number of parameters.

Conclusions:

  • SOAP offers an efficient and robust solution for VQE parameter optimization.
  • The method shows promise for practical applications in quantum chemistry.
  • Experimental validation on a superconducting quantum computer confirms SOAP's effectiveness.