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• W2517233404 abstract "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 annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an {it instance-dependent} effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to $k$-step contrastive divergence (CD-$k$) with $k$ up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here." @default.
• W2517233404 created "2016-09-16" @default.
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• W2517233404 date "2016-08-09" @default.
• W2517233404 modified "2023-10-12" @default.
• W2517233404 title "Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning" @default.
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• W2517233404 doi "https://doi.org/10.1103/physreva.94.022308" @default.
• W2517233404 hasPublicationYear "2016" @default.
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