Software techniques for training restricted Boltzmann machines on size-constrained quantum annealing hardware

Frontiers in Computer Science

Computer Science

Jukka K. Nurminen¹  Ilmo Salmenperä² 

[1] Department of Computer Science, University of Helsinki, Helsinki, Finland;null;

关键词: machine learning;    quantum annealing;    restricted Boltzmann machines;    quantum sampling;    dropout method;   

DOI  :  10.3389/fcomp.2023.1286591

 received in 2023-08-31, accepted in 2023-09-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

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, the 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. We propose the use of multiple software techniques such as 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 the sampling process hindered the performance of the trained models. This means that further research into the behavior of quantum sampling needs to be done to apply quantum annealing to training tasks of more complicated RBM models.

【 授权许可】

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