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

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Frontiers in Computer Science Pub Date : 2023-10-16 DOI:10.3389/fcomp.2023.1286591
Ilmo Salmenperä, Jukka K. Nurminen
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Abstract

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.
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在尺寸受限量子退火硬件上训练受限玻尔兹曼机的软件技术
受限玻尔兹曼机是一种常见的机器学习模型,它可以在训练过程中利用量子退火装置作为量子采样器。虽然这种方法已经显示出作为经典采样方法的替代方案的希望,但量子退火硬件的局限性,例如量子位的数量和量子位之间缺乏连接,仍然对大规模采用构成障碍。我们建议使用多种软件技术,如dropout方法,被动标记和并行化技术来解决这些硬件限制。研究发现,在某些情况下,使用这些技术和量子采样显示出与经典采样相当的结果,而在其他情况下,采样过程的复杂性增加阻碍了训练模型的性能。这意味着需要进一步研究量子采样的行为,以便将量子退火应用于更复杂的RBM模型的训练任务。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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