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We present a tensorization algorithm for constructing tensor train representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the tensor train representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing tensor trains in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks.

In this talk, I will explain the concept of fault tolerance, which ensures reliable quantum computation. Building on recent advancements in mixed-state phases of matter, I introduce a new diagnostic called the spacetime Markov length. The divergence of this length scale signals the intrinsic breakdown of fault tolerance.

In the panel discussion, each panelist will showcase examples of well-written papers and share one essential "do" and one important "don't" for crafting effective scientific papers. The session will include an open Q&A segment to encourage audience engagement.