Error Mitigation for Thermodynamic Computing
CoRR(2024)
摘要
While physics-based computing can offer speed and energy efficiency compared
to digital computing, it also is subject to errors that must be mitigated. For
example, many error mitigation methods have been proposed for quantum
computing. However this error mitigation framework has yet to be applied to
other physics-based computing paradigms. In this work, we consider
thermodynamic computing, which has recently captured attention due to its
relevance to artificial intelligence (AI) applications, such as probabilistic
AI and generative AI. A key source of errors in this paradigm is the
imprecision of the analog hardware components. Here, we introduce a method that
reduces the overall error from a linear to a quadratic dependence (from
ϵ to ϵ^2) on the imprecision ϵ, for Gaussian
sampling and linear algebra applications. The method involves sampling from an
ensemble of imprecise distributions associated with various rounding events and
then merging these samples. We numerically demonstrate the scalability of this
method for dimensions greater than 1000. Finally, we implement this method on
an actual thermodynamic computer and show 20% error reduction for matrix
inversion; the first thermodynamic error mitigation experiment.
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