diff --git a/backends/qualcomm/_passes/__init__.py b/backends/qualcomm/_passes/__init__.py
index ca1aa78ef17..bf99c34f1bf 100644
--- a/backends/qualcomm/_passes/__init__.py
+++ b/backends/qualcomm/_passes/__init__.py
@@ -8,7 +8,6 @@
 from .annotate_quant_attrs import AnnotateQuantAttrs
 from .annotate_stack import AnnotateStack
 from .annotate_unbind import AnnotateUnbind
-from .convert_bmm_to_matmul import ConvertBmmToMatmul
 from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d
 from .convert_square_to_pow import ConvertSquareToPow
 from .decompose_any import DecomposeAny
@@ -45,7 +44,6 @@
     AnnotateQuantAttrs,
     AnnotateStack,
     AnnotateUnbind,
-    ConvertBmmToMatmul,
     ConvertConv1dToConv2d,
     ConvertSquareToPow,
     DecomposeAny,
diff --git a/backends/qualcomm/_passes/convert_bmm_to_matmul.py b/backends/qualcomm/_passes/convert_bmm_to_matmul.py
deleted file mode 100644
index 84e1ff26aa1..00000000000
--- a/backends/qualcomm/_passes/convert_bmm_to_matmul.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# Copyright (c) Qualcomm Innovation Center, Inc.
-# All rights reserved
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-import operator
-from collections import Counter
-from typing import List
-
-import torch
-from executorch.exir.dialects._ops import ops as exir_ops
-from executorch.exir.pass_base import ExportPass, PassResult
-from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
-
-
-class ConvertBmmToMatmul(ExportPass):
-    """
-    Replace bmm to matmul, because bmm is eqaul to matmul in QNN.
-    Handle missing quantization tag for bmm op.
-    """
-
-    view_copy = exir_ops.edge.aten.view_copy.default
-    expand_copy = exir_ops.edge.aten.expand_copy.default
-    clone = exir_ops.edge.aten.clone.default
-    bmm = exir_ops.edge.aten.bmm.default
-    matmul = exir_ops.edge.aten.matmul.default
-    patterns = [
-        {expand_copy: 2, view_copy: 3, bmm: 1},
-        {expand_copy: 2, view_copy: 3, bmm: 1, clone: 1},
-        {bmm: 1},
-    ]
-
-    def __init__(self):
-        super(ConvertBmmToMatmul, self).__init__()
-
-    def _get_ordered_inputs(
-        self, inputs: List[torch.fx.Node], output: torch.fx.Node
-    ) -> List[torch.fx.Node]:
-        bmm_inputs = []
-        for arg in output.args:
-            while arg not in inputs:
-                arg = arg.args[0]
-            bmm_inputs.append(arg)
-        return bmm_inputs
-
-    def call(self, graph_module: torch.fx.GraphModule):
-        graph = graph_module.graph
-        partitions = get_source_partitions(
-            graph,
-            [operator.matmul, torch.matmul, torch.bmm, torch.ops.aten.matmul.default],
-        )
-        for _, src_partitions in partitions.items():
-            for src_partition in src_partitions:
-                op_cnt = Counter([n.target for n in src_partition.nodes])
-                if op_cnt not in self.patterns:
-                    raise AssertionError(
-                        "Found a new pattern needed be converted to linear op"
-                    )
-
-                inputs = src_partition.input_nodes
-                bmm_node = [n for n in src_partition.nodes if n.target == self.bmm][0]
-                output = src_partition.output_nodes[0]
-                # the order of src_partition.inputs is not guaranteed.
-                lhs, rhs = self._get_ordered_inputs(inputs, bmm_node)
-                with graph_module.graph.inserting_before(output):
-                    # replace bmm to matmul, because bmm is eqaul to matmul in qnn.
-                    matmul_node = graph.create_node(
-                        "call_function", self.matmul, (lhs, rhs)
-                    )
-                    matmul_node.meta = output.meta
-                    for user in output.users.copy():
-                        user.replace_input_with(output, matmul_node)
-
-        graph.eliminate_dead_code()
-        graph_module.recompile()
-        return PassResult(graph_module, True)
diff --git a/backends/qualcomm/_passes/qnn_pass_manager.py b/backends/qualcomm/_passes/qnn_pass_manager.py
index bb6a4dd0a67..a46f2ac53ce 100644
--- a/backends/qualcomm/_passes/qnn_pass_manager.py
+++ b/backends/qualcomm/_passes/qnn_pass_manager.py
@@ -13,7 +13,6 @@
     AnnotateQuantAttrs,
     AnnotateStack,
     AnnotateUnbind,
-    ConvertBmmToMatmul,
     ConvertConv1dToConv2d,
     ConvertSquareToPow,
     DecomposeAny,
@@ -80,7 +79,6 @@ def get_capture_program_passes():
         (AnnotateQuantAttrs, True),
         (AnnotateStack, True),
         (AnnotateUnbind, True),
-        (ConvertBmmToMatmul, True),
         (ConvertConv1dToConv2d, True),
         (DecomposeAny, True),
         (DecomposeColIm, True),
diff --git a/backends/qualcomm/_passes/utils.py b/backends/qualcomm/_passes/utils.py
index 898e2d5b1f6..ef52d2c190a 100755
--- a/backends/qualcomm/_passes/utils.py
+++ b/backends/qualcomm/_passes/utils.py
@@ -64,7 +64,6 @@ def get_passes_dependency_for_capture_program():
         AnnotateQuantAttrs,
         AnnotateStack,
         AnnotateUnbind,
-        ConvertBmmToMatmul,
         ConvertConv1dToConv2d,
         DecomposeAny,
         DecomposeColIm,
@@ -85,12 +84,10 @@ def get_passes_dependency_for_capture_program():
         AnnotateAdaptiveAvgPool1D: [RemoveRedundancy],
         AnnotateQuantAttrs: [
             RecomposePixelUnshuffle,
-            ConvertBmmToMatmul,
             RemoveRedundancy,
         ],
         AnnotateStack: [RemoveRedundancy],
         AnnotateUnbind: [RemoveRedundancy],
-        ConvertBmmToMatmul: [RecomposePixelUnshuffle],
         DecomposeAny: [RemoveRedundancy],
         DecomposeColIm: [FoldQDQ],
         DecomposeLinalgVectorNorm: [RemoveRedundancy],
diff --git a/backends/qualcomm/partition/utils.py b/backends/qualcomm/partition/utils.py
index 02318debfa6..4eee818efe5 100644
--- a/backends/qualcomm/partition/utils.py
+++ b/backends/qualcomm/partition/utils.py
@@ -50,6 +50,7 @@ def get_skip_decomp_table() -> List[torch._ops.OperatorBase]:
         torch.ops.aten.instance_norm.default,
         torch.ops.aten.leaky_relu.default,
         torch.ops.aten.linear.default,
+        torch.ops.aten.matmul.default,
         torch.ops.aten.pixel_shuffle.default,
         torch.ops.aten.pixel_unshuffle.default,
         torch.ops.aten.prelu.default,