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A stochastic quantum program synthesis framework based on Bayesian optimization.

Yao Xiao1,2, Shahin Nazarian3, Paul Bogdan4

  • 1University of Southern California, Los Angeles, CA, 90089, USA.

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This summary is machine-generated.

BayeSyn uses enhanced stochastic synthesis and Bayesian optimization to automatically create quantum programs. This approach efficiently generates lower-cost quantum programs by optimizing key parameters.

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Area of Science:

  • Quantum Computing
  • Computer Science

Background:

  • Classical Von Neumann architectures struggle with NP-complete problems.
  • Quantum computing offers potential exponential speedups for complex computations.

Purpose of the Study:

  • To present BayeSyn, an automated system for generating quantum programs.
  • To address the challenge of efficiently creating quantum algorithms from high-level specifications.

Main Methods:

  • Utilizes enhanced stochastic program synthesis to explore the program space.
  • Employs Bayesian optimization to fine-tune hyperparameters for synthesis.
  • Focuses on generating programs under specific constraints.

Main Results:

  • Stochastic synthesis efficiently identifies lower-cost programs in high-dimensional spaces.
  • Bayesian optimization proves crucial for tuning synthesis hyperparameters.
  • BayeSyn successfully generates suitable quantum programs.

Conclusions:

  • Automated quantum program synthesis is feasible and efficient.
  • The combination of stochastic synthesis and Bayesian optimization is effective.
  • Parameter tuning is critical for optimal quantum program generation.