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Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
Marcello Benedetti, John Realpe-Gómez, Rupak Biswas +1
An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum a…
Determination and correction of persistent biases in quantum annealers
Alejandro Perdomo-Ortiz, Bryan O'Gorman, Joseph Fluegemann +2
Calibration of quantum computing technologies is essential to the effective utilization of their quantum resources. Specifically, the performance of quantum annealers is likely to…
A Performance Estimator for Quantum Annealers: Gauge selection and Parameter Setting
Alejandro Perdomo-Ortiz, Joseph Fluegemann, Rupak Biswas +1
With the advent of large-scale quantum annealing devices, several challenges have emerged. For example, it has been shown that the performance of a device can be significantly affe…