Software techniques for training restricted Boltzmann machines on size-constrained quantum annealing hardware - Salmenperä, Ilmo; Nurminen, Jukka K. (Frontiers in Computer Science, 2023)
Restricted Boltzmann Machines are common machine learning models that can utilize quantum annealing devices in their training processes as quantum samplers. While this approach has shown promise as an alternative to classical sampling methods, limitations of quantum annealing hardware, such as the number of qubits and the lack of connectivity between the qubits, still pose a barrier to wide-scale adoption. This study's key contribution is evaluating software techniques such as the unit dropout method, passive labeling, and parallelization techniques for addressing these hardware limitations. The study found that using these techniques along with quantum sampling showed comparable results to its classical counterparts in certain contexts, while in others the increased complexity of sampling process hindered the performance of the trained models.