Method for generating synthetic data

Abstract

The present disclosure pertains to a computer-implemented method for generating synthetic data. The method comprises: providing a tensor network including a plurality of tensors and a training dataset including at least one training data string; training the tensor network with respect to the training dataset by gradient descent, comprising the following steps: determining a tensor network gradient from the tensor network, the tensor network gradient being evaluated using the training dataset; applying noise to the tensor network gradient; and adjusting the tensor network based on the tensor network gradient. The method further comprises generating, from the tensor network, synthetic data including a synthetic data string. Each component to be sampled of the synthetic data string is generated according to a sample probability which is a marginal probability for the component or a conditional probability conditioned on at least one value of a further component of the synthetic data string; and at least one of determining the marginal probability and determining the conditional probability comprises separating, for the component, a corresponding partial tensor network from the tensor network and determining a squared norm of the corresponding partial tensor network. Further, a data processing system, a computer program product, and a computer-readable medium are disclosed.

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