Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Regression for Operator Dynamics   Determining the dynamics of the expectation values for operators acting on a
quantum many-body (QMB) system is a challenging task. Matrix product states
(MPS) have traditionally been the "go-to" models for these systems because
calculating expectation values in this representation can be done with relative
simplicity and high accuracy. However, such calculations can become
computationally costly when extended to long times. Here, we present a solution
for efficiently extending the computation of expectation values to long time
intervals. We utilize a multi-layer perceptron (MLP) model as a tool for
regression on MPS expectation values calculated within the regime of short time
intervals. With this model, the computational cost of generating long-time
dynamics is significantly reduced, while maintaining a high accuracy. These
results are demonstrated with operators relevant to quantum spin models in one
spatial dimension.

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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an