diff --git a/python/tvm/relay/transform/fake_quantization_to_integer.py b/python/tvm/relay/transform/fake_quantization_to_integer.py
index 1adde9a4a430..70e4ad9ceace 100644
--- a/python/tvm/relay/transform/fake_quantization_to_integer.py
+++ b/python/tvm/relay/transform/fake_quantization_to_integer.py
@@ -156,6 +156,25 @@ def conv2d(expr, type_map):
     return [out, TensorAffineType(conv_scale, conv_zp, out.attrs.out_dtype, out_axis.value)]
 
 
+@register_fake_quantization_to_integer("nn.conv2d_transpose")
+def conv2d_transpose(expr, type_map):
+    """Rewrite a conv2d_transpose op"""
+    attrs = {**expr.attrs}
+    attrs.pop("out_dtype")
+    x, weight = expr.args
+    x_t = type_map[x]
+    w_t = type_map[weight]
+    conv_scale = fold_constant(x_t.scale * w_t.scale)
+    conv_zp = get_zeros(conv_scale)
+
+    out = relay.qnn.op.conv2d_transpose(
+        x, weight, x_t.zero_point, w_t.zero_point, x_t.scale, w_t.scale, **attrs
+    )
+    out_layout = attrs["out_layout"] if attrs["out_layout"] != "" else attrs["data_layout"]
+    out_axis = bijective_layout(out_layout, "NCHW").backward_index(list(range(4)))[1]
+    return [out, TensorAffineType(conv_scale, conv_zp, out.attrs.out_dtype, out_axis.value)]
+
+
 @register_fake_quantization_to_integer("nn.dense")
 def dense(expr, type_map):
     """Rewrite a dense op"""
diff --git a/tests/python/relay/test_pass_fake_quantization_to_integer.py b/tests/python/relay/test_pass_fake_quantization_to_integer.py
index c49d837ed920..07413b83de93 100644
--- a/tests/python/relay/test_pass_fake_quantization_to_integer.py
+++ b/tests/python/relay/test_pass_fake_quantization_to_integer.py
@@ -89,6 +89,28 @@ def test_fake_quantize_conv_per_channel():
         compare_fq_to_int(op, [x_np, w_np], allow_rounding_error=True)
 
 
+def test_fake_quantize_transposeconv():
+    for out_dtype in ["int8", "uint8"]:
+        x = relay.var("x", shape=[1, 3, 224, 224], dtype="int8")
+        w = relay.var("w", shape=[3, 16, 5, 5], dtype="int8")
+        one = relay.const(1.0)
+        zero = relay.const(0)
+
+        op = relay.op.nn.conv2d_transpose(
+            relay.qnn.op.dequantize(x, relay.const(2.0), zero),
+            relay.qnn.op.dequantize(w, relay.const(0.5), zero),
+            kernel_size=[5, 5],
+            data_layout="NCHW",
+            kernel_layout="IOHW",
+        )
+        op = relay.qnn.op.quantize(op, one, zero, out_dtype=out_dtype)
+
+        x_np = np.random.randint(-128, 127, size=[1, 3, 224, 224], dtype="int8")
+        w_np = np.random.randint(-128, 127, size=[3, 16, 5, 5], dtype="int8")
+
+        compare_fq_to_int(op, [x_np, w_np])
+
+
 def test_fake_quantize_dense():
     for out_dtype in ["int8", "uint8"]:
         x = relay.var("x", shape=[128, 64], dtype="int8")