diff --git a/onnxscript/function_libs/torch_lib/ops/quantized_decomposed.py b/onnxscript/function_libs/torch_lib/ops/quantized_decomposed.py
index 92962a9ea6..ae82f1572b 100644
--- a/onnxscript/function_libs/torch_lib/ops/quantized_decomposed.py
+++ b/onnxscript/function_libs/torch_lib/ops/quantized_decomposed.py
@@ -11,9 +11,12 @@
 
 from __future__ import annotations
 
+from typing import Optional
+
 from onnxscript.function_libs.torch_lib.ops import common
 from onnxscript.function_libs.torch_lib.registration import torch_op
 from onnxscript.onnx_opset import opset18 as op
+from onnxscript.onnx_opset import opset23 as op23
 from onnxscript.onnx_types import TensorType
 
 
@@ -61,3 +64,63 @@ def quantized_decomposed_dequantize_per_tensor(
         return dequantized
     assert out_dtype > 0, f"out_dtype must be -1 or > 0 not {out_dtype}"
     return op.Cast(dequantized, to=out_dtype)
+
+
+@torch_op(
+    (
+        "quantized_decomposed::quantize_per_channel",
+        "quantized_decomposed::quantize_per_channel.tensor",
+        "quantized_decomposed::quantize_per_channel.tensor2",
+    ),
+    trace_only=True,
+)
+def quantized_decomposed_quantize_per_channel(
+    input: TensorType,
+    scales: TensorType,
+    zero_points: TensorType,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: int,
+) -> TensorType:
+    """Affine per channel quantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from floating point to quantized values.
+
+    Uses ONNX QuantizeLinear with per-axis quantization support.
+    """
+    # Use opset23 for per-axis quantization support
+    return op23.QuantizeLinear(input, scales, zero_points, axis=axis, output_dtype=dtype)
+
+
+@torch_op(
+    (
+        "quantized_decomposed::dequantize_per_channel",
+        "quantized_decomposed::dequantize_per_channel.tensor",
+        "quantized_decomposed::dequantize_per_channel.tensor2",
+    ),
+    trace_only=True,
+)
+def quantized_decomposed_dequantize_per_channel(
+    input: TensorType,
+    scales: TensorType,
+    zero_points: Optional[TensorType],
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: int,
+    out_dtype: int = -1,
+) -> TensorType:
+    """Affine per channel dequantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from quantized values to floating point values.
+
+    Uses ONNX DequantizeLinear with per-axis quantization support.
+    """
+    # Use opset23 for per-axis quantization support with optional output_dtype
+    if out_dtype in (-1, None):
+        # Use default output type (same as scales type)
+        return op23.DequantizeLinear(input, scales, zero_points, axis=axis)
+    else:
+        assert out_dtype > 0, f"out_dtype must be -1 or > 0 not {out_dtype}"
+        return op23.DequantizeLinear(
+            input, scales, zero_points, axis=axis, output_dtype=out_dtype
+        )