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Creators/Authors contains: "Viszlai, Joshua"

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  1. ; ; ; ; ; (, ACM)
    Free, publicly-accessible full text available June 20, 2026
  2. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available March 1, 2026
  3. ; ; ; ; ; ; (, ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating System)
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  4. ; ; ; ; ; (, 2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST))
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  5. ; ; ; ; (, 2022 IEEE International Conference on Quantum Computing and Engineering (QCE))
    Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices. We apply these ideas to Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs state sampling are the main computational bottle-neck during training. By using a coreset in place of the full data set, we try to minimize the number of steps needed and accelerate the overall training time. In a regime where computational time on quantum computers is a precious resource, we propose this might lead to substantial practical savings. We evaluate this approach on 6x6 binary images from an augmented bars and stripes data set using a QBM with 36 visible units and 8 hidden units. Using an Inception score inspired metric, we compare QBM training times with and without using coresets. 
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  6. ; ; ; ; ; ; ; ; ; ; et al (, 2021 IEEE International Conference on Quantum Computing and Engineering (QCE))
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