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Found 3 papers, 1 papers with code
Bayesian inference via rejection filtering
We provide a method for approximating Bayesian inference using rejection sampling.
Quantum Inspired Training for Boltzmann Machines
We present an efficient classical algorithm for training deep Boltzmann machines (DBMs) that uses rejection sampling in concert with variational approximations to estimate the gradients of the training objective function.
Accelerated Randomized Benchmarking
Here, we bound the resources required for benchmarking and show that, with prior information, we can achieve several orders of magnitude better accuracy than in traditional approaches to benchmarking.
Quantum Physics
