Fig. 2: Comparisons of stochastic gradient descent (SGD) and variational generative optimization network (VGON) in generating states with large gaps. | Communications Physics

Fig. 2: Comparisons of stochastic gradient descent (SGD) and variational generative optimization network (VGON) in generating states with large gaps.

From: Variational optimization for quantum problems using deep generative networks

Fig. 2: Comparisons of stochastic gradient descent (SGD) and variational generative optimization network (VGON) in generating states with large gaps.

a Most of the 79,663 random initial states for SGD exhibit small gaps around 0.0036, while after optimization 1.52% of states have gaps larger than 0.08, which is indicated by the dashed line. The largest gap is 0.083722. b Over 98.59% of the 100,000 states generated by a trained VGON model have gaps larger than 0.08, which is presented by the dashed line. In particular, over 50% of these states are tightly centered around 0.0837.

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