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docs: explain save vs save_weights for TFQ layers #1120

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docs: explain save vs save_weights for TFQ layers
SripriyaDevarapalli Oct 13, 2026
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docs: clarify saving TFQ layers
SripriyaDevarapalli Oct 18, 2026
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18 changes: 18 additions & 0 deletions docs/design.md
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Expand Up @@ -87,6 +87,24 @@ operations. Additionally, you can create a layer with a high degree of
complexity supporting batch circuit, batch control parameter value, and perform
batch readout operations. See `tfq.layers.Sample` for an example.

#### Saving and loading models

Saving a complete Keras model with `model.save()` is not supported for models containing TFQ layers, including `tfq.layers.PQC` and `tfq.layers.ControlledPQC`. These layers accept quantum objects, such as `cirq.Circuit`, `cirq.PauliSum`, and `sympy.Symbol`, that are not
JSON-serializable, and the layers do not implement `get_config()` to serialize their configurations.

Depending on the serialization path, calling `model.save()` may raise a `NotImplementedError` indicating that the layer must override
`get_config()`, a `TypeError` for non-JSON-serializable `call()`
arguments such as `sympy.Symbol` or `cirq.PauliSum`, or a `NotEncodableError` when tracing layer `call` defaults for `SavedModel` export. This limitation is not specific to a particular TFQ, TensorFlow, or Keras version.

As an alternative, `model.save_weights()` saves the model's weights without saving the complete model configuration. Recreate the same model architecture before loading the saved weights:

```python
model.save_weights("my_model.weights.h5")

new_model = build_model() # Recreate the same model architecture.
new_model.load_weights("my_model.weights.h5")
```

### Differentiators

Unlike many TensorFlow operations, observables in quantum circuits do not have
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