Procedural bars; no real cat/dog data
Rows show requested classes in this order: vertical bar, horizontal bar. Each class has eight samples. Before/after use identical noise and labels. These are actual model outputs.
The three models use the same data split, minibatch order, latent prior, discriminator initialization, losses, learning rate and update count. Generator architecture and parameter counts differ. Pixel diagnostics describe averages and variation; they do not assess whether an image looks like a cat or dog. GAN losses are not image-quality scores.
Open the gate-by-gate circuit explanation
{
"kind": "patch",
"generator_config": {
"kind": "patch",
"image_size": 8,
"channels": 1,
"latent_dim": 4,
"num_classes": 2,
"patch_size": 4,
"depth": 1,
"seed": 7
},
"trainer_config": {
"learning_rate": 0.002,
"discriminator_hidden": 64,
"seed": 7
},
"session_steps": 200,
"total_steps": 200,
"generator_parameters": 95,
"quantum_parameters": 60,
"discriminator_parameters": 8513,
"session_training_seconds": 67.08659580499989,
"timing_scope": "Full CPU training loop including parameter shifts and optimizers; no setup/export",
"before": {
"vertical bar": {
"class_mean_pixel_mae": 0.15547630190849304,
"generated_pixel_std": 0.12237796187400818
},
"horizontal bar": {
"class_mean_pixel_mae": 0.18267789483070374,
"generated_pixel_std": 0.1528683304786682
}
},
"after": {
"vertical bar": {
"class_mean_pixel_mae": 0.14255580306053162,
"generated_pixel_std": 0.18107548356056213
},
"horizontal bar": {
"class_mean_pixel_mae": 0.12114449590444565,
"generated_pixel_std": 0.16909977793693542
}
},
"final_losses": {
"discriminator_loss": 0.03854788839817047,
"generator_loss": 5.725915431976318,
"generated_mean": 0.11700618267059326,
"step": 200,
"epoch": 15
},
"parameter_effect": {
"parameter": "quantum circuit 0 / w0",
"value": 0.13246725499629974,
"delta": 0.25,
"max_abs_pixel_sensitivity": 0.39973896741867065
}
}
Open the gate-by-gate circuit explanation
{
"kind": "hybrid",
"generator_config": {
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"image_size": 8,
"channels": 1,
"latent_dim": 4,
"num_classes": 2,
"n_qubits": 4,
"depth": 1,
"hidden": 64,
"seed": 7
},
"trainer_config": {
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"discriminator_hidden": 64,
"seed": 7
},
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"total_steps": 200,
"generator_parameters": 4520,
"quantum_parameters": 12,
"discriminator_parameters": 8513,
"session_training_seconds": 12.385896855999817,
"timing_scope": "Full CPU training loop including parameter shifts and optimizers; no setup/export",
"before": {
"vertical bar": {
"class_mean_pixel_mae": 0.3640994429588318,
"generated_pixel_std": 0.012275784276425838
},
"horizontal bar": {
"class_mean_pixel_mae": 0.36683833599090576,
"generated_pixel_std": 0.015466976910829544
}
},
"after": {
"vertical bar": {
"class_mean_pixel_mae": 0.22409045696258545,
"generated_pixel_std": 0.007094826549291611
},
"horizontal bar": {
"class_mean_pixel_mae": 0.2919803559780121,
"generated_pixel_std": 0.005506881978362799
}
},
"final_losses": {
"discriminator_loss": 0.0425606369972229,
"generator_loss": 4.751199722290039,
"generated_mean": 0.2072010040283203,
"step": 200,
"epoch": 15
},
"parameter_effect": {
"parameter": "quantum circuit 0 / w0",
"value": -0.0628545880317688,
"delta": 0.25,
"max_abs_pixel_sensitivity": 0.02759385108947754
}
}
{
"kind": "classical",
"generator_config": {
"kind": "classical",
"image_size": 8,
"channels": 1,
"latent_dim": 4,
"num_classes": 2,
"hidden": 64,
"seed": 7
},
"trainer_config": {
"learning_rate": 0.002,
"discriminator_hidden": 64,
"seed": 7
},
"session_steps": 200,
"total_steps": 200,
"generator_parameters": 8768,
"quantum_parameters": 0,
"discriminator_parameters": 8513,
"session_training_seconds": 0.3390391989996715,
"timing_scope": "Full CPU training loop including parameter shifts and optimizers; no setup/export",
"before": {
"vertical bar": {
"class_mean_pixel_mae": 0.35402804613113403,
"generated_pixel_std": 0.016369622200727463
},
"horizontal bar": {
"class_mean_pixel_mae": 0.35221701860427856,
"generated_pixel_std": 0.01611008122563362
}
},
"after": {
"vertical bar": {
"class_mean_pixel_mae": 0.18951523303985596,
"generated_pixel_std": 0.004129737615585327
},
"horizontal bar": {
"class_mean_pixel_mae": 0.17867916822433472,
"generated_pixel_std": 0.004352353513240814
}
},
"final_losses": {
"discriminator_loss": 0.07428395748138428,
"generator_loss": 4.030426025390625,
"generated_mean": 0.06743431836366653,
"step": 200,
"epoch": 15
}
}
{
"sdk_version": "1.2.0",
"torch_version": "2.14.1+cpu",
"numpy_version": "2.5.4",
"platform": "Linux-6.18.55-x86_64-with-glibc2.39",
"device": "cpu",
"precision": "float32 Torch / complex128 native quantum simulation",
"threads": 1,
"source": "Procedural bars; no real cat/dog data",
"classes": [
"vertical bar",
"horizontal bar"
],
"shape": [
1,
8,
8
],
"training_samples": 52,
"validation_samples": 12,
"training_fingerprint": "6a9c0d441d7c34c2b99f4c690c1c61aa1812456878b6c119cef360959c19aa9a",
"validation_fingerprint": "fd68bb0748acf547909a62b65929b9a81518644746cae7cffd6bb0f93affb3c7",
"seed": 7,
"batch_size": 4,
"learning_rate": 0.002,
"requested_session_steps": 200,
"comparison": "Matched updates/data/D initialization; generator parameter budgets differ",
"diagnostic_definition": "Per-class held-out mean-pixel MAE and generated per-pixel standard deviation; neither is semantic quality"
}