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Boltzmann sampling by diabatic quantum annealing
Ju-Yeon Gyhm1, Gilhan Kim1, Hyukjoon Kwon2
1Seoul National University, Department of Physics and Astronomy & Center for Theoretical Physics, Seoul 08826, Korea.
Physical Review. E
|July 24, 2026
Summary
Diabatic quantum annealing offers a controllable method for Boltzmann sampling, overcoming limitations of traditional quantum annealers. This faster, unitary process accurately samples in high-temperature regimes.
Area of Science:
- Quantum computing
- Computational physics
- Machine learning
Background:
- Boltzmann sampling is crucial for computational frameworks and machine learning algorithms.
- Quantum annealers are explored for Boltzmann sampling but struggle with temperature control due to environmental noise.
Purpose of the Study:
- To introduce diabatic quantum annealing as a controllable and faster alternative for Boltzmann sampling.
- To demonstrate its effectiveness in achieving accurate sampling.
Main Methods:
- Proposed diabatic quantum annealing, a purely unitary process.
- Utilized the ferromagnetic Ising model and Sherrington-Kirkpatrick model for testing.
- Investigated the relationship between annealing rate and effective temperature.
Main Results:
- Diabatic quantum annealing provides precise control over effective temperature.
- The method achieves rapid and accurate Boltzmann sampling.
- Demonstrated success in the high-temperature regime using benchmark models.
Conclusions:
- Diabatic quantum annealing is a viable and controllable Boltzmann sampler.
- It offers advantages over traditional quantum annealers, particularly in temperature control.
- This technique shows promise for machine learning and computational frameworks.
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