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Greedy parameter optimization for diabatic quantum annealing.

Tadashi Kadowaki1, Hidetoshi Nishimori2,3,4

  • 1DENSO CORPORATION, Kounan, Minato-ku, Tokyo 108-0075, Japan.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|December 4, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new quantum annealing method to speed up computations and reduce errors. This approach improves success rates and solution times, especially for shorter annealing durations, making quantum computation more robust.

Keywords:
counterdiabatic drivinggreedy optimizationquantum annealing

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Area of Science:

  • Quantum computation
  • Quantum annealing
  • Ising model

Background:

  • Shorter processing times are crucial in quantum computation to mitigate noise.
  • Quantum annealing (QA) is a promising approach for solving complex optimization problems.

Purpose of the Study:

  • To propose a novel variational method for parameter determination in transverse-field Ising model QA.
  • To enhance the performance of quantum annealing by minimizing noise effects and processing time.

Main Methods:

  • A greedy optimization procedure for the signs of coefficients in the transverse-field Ising model.
  • Utilizing short annealing process outputs for parameter determination.
  • Testing the method on ferromagnetic and spin-glass problems.

Main Results:

  • The proposed method outperforms traditional QA and simulated annealing in success probability and time to solution.
  • Significant improvements were observed, particularly at shorter annealing times.
  • The method effectively reduces the impact of noise.

Conclusions:

  • The developed variational method offers a practical approach to improve quantum annealing efficiency.
  • The non-stoquastic term can be transformed, allowing for feasible experimental realization.
  • This work contributes to advancing quantum annealing for practical applications.