diff --git a/backends/qualcomm/utils/utils.py b/backends/qualcomm/utils/utils.py
index 33be00ed51d..a73fe6944eb 100644
--- a/backends/qualcomm/utils/utils.py
+++ b/backends/qualcomm/utils/utils.py
@@ -337,7 +337,7 @@ def capture_program(
     inputs: Tuple[torch.Tensor],
     custom_pass_config: FrozenSet[str] = frozenset(),
 ) -> exir.ExirExportedProgram:
-    ep = torch.export.export(module, inputs)
+    ep = torch.export.export(module, inputs, strict=True)
     decomposed_ep = ep.run_decompositions(get_decomp_table())
     # We choose call_operator by target in ConvertBinaryOpsWithScalar
     # because it is the same source_fn_stack for MultiheadAttention
@@ -551,7 +551,7 @@ def prepare_subgm(subgm, subgm_name):
 
     fp_node_id_set = fp_node_id_set if fp_node_id_set is not None else set()
     fp_node_op_set = fp_node_op_set if fp_node_op_set is not None else set()
-    graph_module = torch.export.export(nn_module, sample_input).module()
+    graph_module = torch.export.export(nn_module, sample_input, strict=True).module()
     # define node support type
     capability_partitioner = CapabilityBasedPartitioner(
         graph_module,
@@ -664,7 +664,7 @@ def forward(self, *inputs):
                 ).default(inputs)
 
         model = Model()
-        prog = torch.export.export(model, tuple(inputs.values()))
+        prog = torch.export.export(model, tuple(inputs.values()), strict=True)
         # bookkeeping for variables' life cycle
         return {
             "custom_op": custom_op,