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Distilling optimization tasks into model training

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Nicholas T
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Hi there. I am curious if there is much prior work related to distilling forms of annealing optimization constraints into a neural network training operation such as to approximate such integration into subsequent inference operations. This question is partly inspired by review of the recent ICLR paper "Learning where and when to reason in neuro-symbolic inference" by Cornelio et al which address similar matters for other forms of constraints.

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    Tanjid I  (Report)

    Hi Nicholas,

    Thank you for your question regarding the integration of annealing optimization constraints into neural network training operations. There has been some prior research in this area, and the following two papers that you may find beneficial:

    1. "Approximating Quantum Annealing with Analog DNN" by Chen et al. (https://arxiv.org/abs/1912.02119)
    2. "Using a Convolutional Neural Network to Learn Adiabatic Quantum Optimization Parameters" by Wang et al. (https://ieeexplore.ieee.org/abstract/document/9432948)

    Please let me know if you have any further questions or if there is anything else that we can assist you with.

    Best Regards,
    Tanjid

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