diff --git a/src/brevitas_examples/imagenet_classification/ptq/learned_round_utils.py b/src/brevitas_examples/imagenet_classification/ptq/learned_round_utils.py
index 00f8e472e..7a5a283ea 100644
--- a/src/brevitas_examples/imagenet_classification/ptq/learned_round_utils.py
+++ b/src/brevitas_examples/imagenet_classification/ptq/learned_round_utils.py
@@ -32,7 +32,9 @@
 
 from brevitas import config
 from brevitas.core.function_wrapper.learned_round import LearnedRoundSte
+from brevitas.graph.calibrate import disable_return_quant_tensor
 from brevitas.graph.calibrate import DisableEnableQuantization
+from brevitas.graph.calibrate import restore_return_quant_tensor
 from brevitas.inject.enum import FloatToIntImplType
 from brevitas.inject.enum import LearnedRoundImplType
 from brevitas.nn.quant_layer import QuantWeightBiasInputOutputLayer as QuantWBIOL
@@ -185,6 +187,7 @@ def save_inp_out_data(
         disable_quant_class = DisableEnableQuantization()
         disable_quant_class.disable_act_quantization(model, False)
         disable_quant_class.disable_param_quantization(model, False)
+        return_quant_tensor_state = disable_return_quant_tensor(model)
     device = next(model.parameters()).device
     data_saver = DataSaverHook(store_output=store_out)
     handle = module.register_forward_hook(data_saver)
@@ -213,4 +216,5 @@ def save_inp_out_data(
     if disable_quant:
         disable_quant_class.enable_act_quantization(model, False)
         disable_quant_class.enable_param_quantization(model, False)
+        restore_return_quant_tensor(model, return_quant_tensor_state)
     return cached
diff --git a/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py b/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
index fd5e5c386..8a70e29ba 100644
--- a/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
+++ b/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
@@ -47,6 +47,11 @@ def parse_type(v, default_type):
         return default_type(v)
 
 
+def validate_args(args):
+    if args.learned_round:
+        assert args.target_backend == "layerwise", "Currently, learned round is only supported with target-backend=layerwise"
+
+
 model_names = sorted(
     name for name in torchvision.models.__dict__ if name.islower() and not name.startswith("__") and
     callable(torchvision.models.__dict__[name]) and not name.startswith("get_"))
@@ -280,6 +285,7 @@ def generate_ref_input(args, device, dtype):
 
 def main():
     args = parser.parse_args()
+    validate_args(args)
     dtype = getattr(torch, args.dtype)
 
     random.seed(SEED)
diff --git a/tests/brevitas/graph/test_calibration.py b/tests/brevitas/graph/test_calibration.py
index 10d8f7e7c..fbfc76842 100644
--- a/tests/brevitas/graph/test_calibration.py
+++ b/tests/brevitas/graph/test_calibration.py
@@ -2,8 +2,10 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import math
+from typing import Union
 
 from hypothesis import given
+import pytest
 import pytest_cases
 from pytest_cases import fixture
 import torch
@@ -11,12 +13,16 @@
 
 from brevitas.graph.calibrate import bias_correction_mode
 from brevitas.graph.calibrate import calibration_mode
+from brevitas.graph.calibrate import disable_return_quant_tensor
+from brevitas.graph.calibrate import DisableEnableQuantization
 from brevitas.graph.calibrate import load_quant_model_mode
+from brevitas.graph.calibrate import restore_return_quant_tensor
 from brevitas.inject.enum import RestrictValueType
 import brevitas.nn as qnn
 from brevitas.quant import Int8ActPerTensorFixedPoint
 from brevitas.quant.experimental.float import Fp8e4m3ActPerTensorFloat
 from brevitas.quant.scaled_int import Int8ActPerTensorFloat
+from brevitas.quant_tensor import QuantTensor
 # Use custom implementation of kthvalue as work around to (b)float16 kernel limitations
 from brevitas.utils.torch_utils import kthvalue
 from tests.brevitas.hyp_helper import float_tensor_random_size_st
@@ -307,3 +313,54 @@ def forward(self, inp):
     for m in model.modules():
         if isinstance(m, qnn.QuantLinear):
             assert m.bias is None
+
+
+class TestDisableEnableQuantization():
+
+    @fixture
+    def model(self):
+
+        class TestQuantModel(nn.Module):
+
+            def __init__(self) -> None:
+                super().__init__()
+                # Note that the
+                self.act = qnn.QuantIdentity(return_quant_tensor=True,)
+
+            def forward(self, x: Union[torch.Tensor,
+                                       QuantTensor]) -> Union[torch.Tensor, QuantTensor]:
+                return self.act(x)
+
+        model = TestQuantModel()
+        model.eval()
+        return model
+
+    def test_disable_enable_quantization(self, model):
+        disable_quant_class = DisableEnableQuantization()
+        # Sample input, not relevant to the task
+        sample_input = torch.rand(size=(2, 3))
+
+        # (1) Verify that an appropiate tensor is returned
+        quant_out = model(sample_input)
+        assert isinstance(quant_out, QuantTensor) and quant_out.is_valid
+
+        # (2) Disable activation quantisation
+        disable_quant_class.disable_act_quantization(model, is_training=False)
+        # Verify that an error is raised when return_quant_tensor=True and
+        # disable_return_quant_tensor is not applied
+        with pytest.raises(
+                AssertionError,
+                match="QuantLayer is not correctly configured, check if warnings were raised"):
+            model(sample_input)
+
+        # (3) Disable return quant tensor and verify no error is raised
+        return_quant_tensor_state = disable_return_quant_tensor(model)
+        fp_out = model(sample_input)
+        assert isinstance(fp_out, torch.Tensor)
+
+        # (4) Enable again activation quantisation and check that a QuantTensor
+        # is returned
+        restore_return_quant_tensor(model, return_quant_tensor_state)
+        disable_quant_class.enable_act_quantization(model, is_training=False)
+        quant_out = model(sample_input)
+        assert isinstance(quant_out, QuantTensor) and quant_out.is_valid