A quantum MCMC algorithm leveraging the MBL phase and its thermal-to-localized transition to tune acceptance rates and sample thermal distributions on programmable quantum simulators for combinatorial optimization.
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Tempered posteriors combined with Wang-Landau sampling identify transition temperatures that optimize predictive performance in Bayesian inference for real-world problems.
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Quantum Markov chain Monte Carlo method with programmable quantum simulators
A quantum MCMC algorithm leveraging the MBL phase and its thermal-to-localized transition to tune acceptance rates and sample thermal distributions on programmable quantum simulators for combinatorial optimization.
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Using Statistical Mechanics to Improve Real-World Bayesian Inference: A New Method Combining Tempered Posteriors and Wang-Landau Sampling
Tempered posteriors combined with Wang-Landau sampling identify transition temperatures that optimize predictive performance in Bayesian inference for real-world problems.