docs: clarify quantized Add + tanh fusion limitation in TFLite

tensorflow_model_optimization/python/core/quantization/README.md

@@ -0,0 +1,53 @@
+## Limitations: Quantized Fused Activations in TFLite
+
+When converting TensorFlow / Keras models to **quantized TFLite models**, not all
+activation functions are currently supported as **fused activations** by the
+TFLite MLIR converter.
+
+### Unsupported Case: Add + Tanh Fusion
+
+Although the TFLite schema defines `TANH` as a possible fused activation,
+the current TFLite MLIR converter **does not fuse `Add` + `tanh`** during
+quantized conversion.
+
+As a result:
+
+- `Add` and `tanh` are emitted as **separate operations**
+- Quantized outputs may **differ from the original floating-point Keras model**
+- This behavior is expected and represents a current limitation
+
+At present, only the following activations are fused during quantized conversion:
+
+- `RELU`
+- `RELU6`
+- `RELU_N1_TO_1`
+
+### Minimal Reproducible Example
+
+```python
+import tensorflow as tf
+import numpy as np
+
+# Simple model with Add + tanh
+inputs = tf.keras.Input(shape=(4,))
+x = tf.keras.layers.Add()([inputs, inputs])
+outputs = tf.keras.activations.tanh(x)
+model = tf.keras.Model(inputs, outputs)
+
+# Representative dataset for quantization
+def representative_dataset():
+    for _ in range(100):
+        yield [np.random.rand(1, 4).astype(np.float32)]
+
+converter = tf.lite.TFLiteConverter.from_keras_model(model)
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = representative_dataset
+converter.target_spec.supported_ops = [
+    tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+]
+converter.inference_input_type = tf.int8
+converter.inference_output_type = tf.int8
+
+tflite_model = converter.convert()
+
+