diff --git a/python/tvm/relay/qnn/op/qnn.py b/python/tvm/relay/qnn/op/qnn.py
index 1717bc42fe946..2e9c0d83148a2 100644
--- a/python/tvm/relay/qnn/op/qnn.py
+++ b/python/tvm/relay/qnn/op/qnn.py
@@ -19,6 +19,7 @@
 
 from __future__ import absolute_import as _abs
 from . import _make
+from tvm import relay
 
 def requantize(data,
                input_scale,
@@ -72,3 +73,55 @@ def requantize(data,
                             output_zero_point,
                             rounding,
                             out_dtype)
+
+def concatenate(data,
+                input_scales,
+                input_zero_points,
+                output_scale,
+                output_zero_point,
+                output_dtype,
+                axis):
+    """Concatenate the quantized input tensors along the given axis.
+
+    Parameters
+    ----------
+    data : Union(List[relay.Expr], Tuple[relay.Expr])
+        A list of quantized tensors
+
+    input_scales : List[float32]
+        A list of scales of quantized tensors
+
+    input_zero_points : List[int32]
+        A list of zero points of quantized tensors
+
+    output_scale : float32
+        A scales of output
+
+    output_zero_point : int32
+        A zero points of output
+
+    axis : int
+        The axis along which the tensors are concatenated.
+
+    Returns
+    -------
+    result: relay.Expr
+        The concatenated tensor
+    """
+
+    data = list(data)
+    requantized_exprs = list(data)
+    # If the output qnn params do not match the input qnn params, we call requantize on the input
+    # params.
+    for idx, quantized_expr in enumerate(data):
+        scale = input_scales[idx]
+        zero_point = input_zero_points[idx]
+        if scale != output_scale or zero_point != output_zero_point:
+            requantized_exprs[idx] = requantize(quantized_expr,
+                                                input_scale=scale,
+                                                input_zero_point=zero_point,
+                                                output_scale=output_scale,
+                                                output_zero_point=output_zero_point,
+                                                out_dtype=output_dtype)
+    # As all tensors now share same qnn params, we can directly call relay concatenate.
+    return relay.concatenate(tuple(requantized_exprs), axis)
diff --git a/tests/python/relay/test_qnn_concatenate.py b/tests/python/relay/test_qnn_concatenate.py
new file mode 100644
index 0000000000000..febc12cde3d90
--- /dev/null
+++ b/tests/python/relay/test_qnn_concatenate.py
@@ -0,0 +1,55 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+import numpy as np
+from tvm import relay
+from tvm.contrib import graph_runtime
+import topi.testing
+
+def test_qnn_concatenate():
+    data_dtype = 'int32'
+    axis = 0
+    x_data = np.arange(-32, 32, 1).reshape(1, 64).astype(data_dtype)
+    y_data = np.arange(-64, 64, 2).reshape(1, 64).astype(data_dtype)
+    x_scale = (62 + 64) / (np.power(2, 32) - 1.0)
+    y_scale = (62 + 64) / (np.power(2, 32) - 1.0)
+
+    x = relay.var("x", shape=(1, 64), dtype=data_dtype)
+    y = relay.var("y", shape=(1, 64), dtype=data_dtype)
+    z = relay.qnn.op.concatenate((x, y),
+                                 input_scales=[x_scale, y_scale],
+                                 input_zero_points=[0, 0],
+                                 output_scale=y_scale,
+                                 output_zero_point=1,
+                                 output_dtype=data_dtype,
+                                 axis=axis)
+
+    func = relay.Function([x, y], z)
+    mod = relay.Module.from_expr(func)
+    mod = relay.transform.Legalize()(mod)
+    func = mod["main"]
+
+    golden_output = np.concatenate((x_data, y_data), axis=axis)
+    golden_output = np.add(1, golden_output)
+
+    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
+    op_res = intrp.evaluate(func)(x_data, y_data)
+    np.testing.assert_equal(op_res.asnumpy(), golden_output)
+
+if __name__ == '__main__':
+    test_qnn_concatenate()