HI, I trained the model of quantum GAN for 800 epochs and the result is the same. How can we solve this problem? Did you obtain good images with your dataset?

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Iteration: 10, Discriminator Loss: 1.313, Generator Loss: 0.624
Iteration: 20, Discriminator Loss: 1.154, Generator Loss: 0.634
Iteration: 30, Discriminator Loss: 0.906, Generator Loss: 0.650
Iteration: 40, Discriminator Loss: 0.761, Generator Loss: 0.677
Iteration: 50, Discriminator Loss: 0.732, Generator Loss: 0.717
Iteration: 60, Discriminator Loss: 0.660, Generator Loss: 0.753
Iteration: 70, Discriminator Loss: 0.630, Generator Loss: 0.786
Iteration: 80, Discriminator Loss: 0.612, Generator Loss: 0.819
Iteration: 90, Discriminator Loss: 0.564, Generator Loss: 0.855
Iteration: 100, Discriminator Loss: 0.535, Generator Loss: 0.891
Iteration: 110, Discriminator Loss: 0.516, Generator Loss: 0.923
Iteration: 120, Discriminator Loss: 0.494, Generator Loss: 0.959
Iteration: 130, Discriminator Loss: 0.465, Generator Loss: 0.996
Iteration: 140, Discriminator Loss: 0.444, Generator Loss: 1.037
Iteration: 150, Discriminator Loss: 0.421, Generator Loss: 1.081
Iteration: 160, Discriminator Loss: 0.400, Generator Loss: 1.121
Iteration: 170, Discriminator Loss: 0.381, Generator Loss: 1.157
Iteration: 180, Discriminator Loss: 0.364, Generator Loss: 1.210
Iteration: 190, Discriminator Loss: 0.340, Generator Loss: 1.261
Iteration: 200, Discriminator Loss: 0.321, Generator Loss: 1.301
Iteration: 210, Discriminator Loss: 0.305, Generator Loss: 1.348
Iteration: 220, Discriminator Loss: 0.288, Generator Loss: 1.395
Iteration: 230, Discriminator Loss: 0.269, Generator Loss: 1.459
Iteration: 240, Discriminator Loss: 0.255, Generator Loss: 1.510
Iteration: 250, Discriminator Loss: 0.234, Generator Loss: 1.575
Iteration: 260, Discriminator Loss: 0.223, Generator Loss: 1.633
Iteration: 270, Discriminator Loss: 0.204, Generator Loss: 1.700
Iteration: 280, Discriminator Loss: 0.193, Generator Loss: 1.756
Iteration: 290, Discriminator Loss: 0.179, Generator Loss: 1.824
Iteration: 300, Discriminator Loss: 0.191, Generator Loss: 1.869
Iteration: 310, Discriminator Loss: 0.160, Generator Loss: 1.925
Iteration: 320, Discriminator Loss: 0.167, Generator Loss: 2.029
Iteration: 330, Discriminator Loss: 0.145, Generator Loss: 2.033
Iteration: 340, Discriminator Loss: 0.132, Generator Loss: 2.104
Iteration: 350, Discriminator Loss: 0.128, Generator Loss: 2.137
Iteration: 360, Discriminator Loss: 0.118, Generator Loss: 2.209
Iteration: 370, Discriminator Loss: 0.109, Generator Loss: 2.289
Iteration: 380, Discriminator Loss: 0.105, Generator Loss: 2.323
Iteration: 390, Discriminator Loss: 0.097, Generator Loss: 2.406
Iteration: 400, Discriminator Loss: 0.093, Generator Loss: 2.446
Iteration: 410, Discriminator Loss: 0.089, Generator Loss: 2.483
Iteration: 420, Discriminator Loss: 0.084, Generator Loss: 2.530
Iteration: 430, Discriminator Loss: 0.077, Generator Loss: 2.623
Iteration: 440, Discriminator Loss: 0.080, Generator Loss: 2.577
Iteration: 450, Discriminator Loss: 0.077, Generator Loss: 2.654
Iteration: 460, Discriminator Loss: 0.074, Generator Loss: 2.664
Iteration: 470, Discriminator Loss: 0.067, Generator Loss: 2.754
Iteration: 480, Discriminator Loss: 0.068, Generator Loss: 2.759
Iteration: 490, Discriminator Loss: 0.065, Generator Loss: 2.776
Iteration: 500, Discriminator Loss: 0.062, Generator Loss: 2.821
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<img src="upload://j93LBZHgBfMUf2o2yJ8J3GNDgn5.png" alt="image.png" width="475" height="505">
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