Building separable approximations for quantum states via neural networks. (arXiv:2112.08055v2 [quant-ph] UPDATED)

Finding the closest separable state to a given target state is a notoriously
difficult task, even more difficult than deciding whether a state is entangled
or separable. To tackle this task, we parametrize separable states with a
neural network and train it to minimize the distance to a given target state,
with respect to a differentiable distance, such as the trace distance or
Hilbert-Schmidt distance. By examining the output of the algorithm, we can
deduce whether the target state is …

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