diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index d185d143c7a6..62f0f4b2dd25 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -1406,7 +1406,10 @@ def _impl_v1(cls, inputs, attr, params):
             inputs[0] *= _expr.const(alpha, dtype=dtype)
         out = _op.nn.dense(inputs[0], inputs[1], units=channels)
         if len(inputs) == 3:
-            out = out + _expr.const(beta, dtype=dtype) * inputs[2]
+            if beta != 1.0:
+                out += _expr.const(float(beta), dtype=dtype) * inputs[2]
+            else:
+                out += inputs[2]
         return out
 
 
diff --git a/python/tvm/relay/transform/fake_quantization_to_integer.py b/python/tvm/relay/transform/fake_quantization_to_integer.py
index 46bdd94ace1a..84b1f33e98cc 100644
--- a/python/tvm/relay/transform/fake_quantization_to_integer.py
+++ b/python/tvm/relay/transform/fake_quantization_to_integer.py
@@ -502,6 +502,37 @@ def register_binary_qnn(op_name, op):
 
     def binary(expr, type_map):
         left, right, left_t, right_t, out_t = get_binary_types(expr, type_map)
+
+        if (
+            op_name == "add"
+            and approx_equal(left_t.scale, right_t.scale)
+            and approx_equal(left_t.zero_point, right_t.zero_point)
+            and tvm.ir.structural_equal(left_t.dtype, right_t.dtype)
+            and left_t.dtype == "int32"
+            and approx_equal(left_t.scale, out_t.scale)
+            and approx_equal(left_t.zero_point, out_t.zero_point)
+            and np.all(out_t.zero_point.data.numpy() == 0)
+        ):
+            # If this add op comes after conv2d or dense, out_t.scale and out_t.zero_point
+            # can be a vector, which is not supported by QNN binary operators.
+            # In particular, the pattern of an `add` op following `dense`, where the addition is
+            # really a bias addtion, can come up often. We identify that pattern and convert it to
+            # `qnn.dense` -> `add`.
+            # To avoid overflow, we do this conversion only when the input data type is 32 bit (bias
+            # addition is typically done in 32 bit).
+            return [left + right, left_t]
+
+        assert (
+            len(out_t.scale.data.shape) == 0
+        ), "The output scale needs to be a scalar, but got a tensor of shape {}".format(
+            out_t.scale.data.shape
+        )
+        assert (
+            len(out_t.zero_point.data.shape) == 0
+        ), "The output zero point needs to be a scalar, but got a tensor of shape {}".format(
+            out_t.zero_point.data.shape
+        )
+
         out = op(
             left,
             right,
diff --git a/src/relay/transforms/fake_quantization_to_integer.cc b/src/relay/transforms/fake_quantization_to_integer.cc
index eb176df5c978..31353d5aa25e 100644
--- a/src/relay/transforms/fake_quantization_to_integer.cc
+++ b/src/relay/transforms/fake_quantization_to_integer.cc
@@ -193,7 +193,7 @@ class SubgraphMutator : public ExprMutator {
       return Mutate(expr);
     } catch (std::exception& e) {
       if (hard_fail_) {
-        throw e;
+        LOG(FATAL) << e.what();
       } else {
         DLOG(INFO) << "Ran into an error rewriting a subgraph, skipping" << expr << std::endl;
         return expr;
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 569bd9d7d653..d384635e42e5 100644
--- a/tests/python/relay/test_pass_fake_quantization_to_integer.py
+++ b/tests/python/relay/test_pass_fake_quantization_to_integer.py
@@ -154,6 +154,41 @@ def test_fake_quantize_dense_per_channel():
         compare_fq_to_int(op, [x_np, w_np], allow_rounding_error=True)
 
 
+def test_fake_quantize_dense_bias():
+    out_dtype = "int8"
+    x = relay.var("x", shape=[128, 64], dtype="int8")
+    w = relay.var("w", shape=[256, 64], dtype="int8")
+    bias = relay.var("bias", shape=[256], dtype="int32")
+    one = relay.const(1.0)
+    zero = relay.const(0)
+    w_scale = np.random.random([256]).astype("float32")
+
+    op = relay.op.nn.dense(
+        relay.qnn.op.dequantize(x, relay.const(2.0), zero),
+        relay.qnn.op.dequantize(
+            w,
+            relay.const(w_scale),
+            zero,
+            axis=0,
+        ),
+        units=256,
+    )
+
+    op += relay.qnn.op.dequantize(
+        bias,
+        relay.const(2.0 * w_scale),
+        zero,
+    )
+
+    op = relay.qnn.op.quantize(op, one, zero, out_dtype=out_dtype)
+
+    x_np = np.random.randint(-128, 127, size=[128, 64], dtype="int8")
+    w_np = np.random.randint(-128, 127, size=[256, 64], dtype="int8")
+    bias_np = np.random.randint(-128, 127, size=[256], dtype="int32")
+
+    compare_fq_to_int(op, [x_np, w_np, bias_np], allow_rounding_error=True)
+
+
 def test_fake_quantize_batch_matmul():
     for out_dtype in ["int8", "uint8"]:
         x = relay.var("x", shape=[1, 128, 64], dtype="int8")
@@ -976,15 +1011,9 @@ def test_fq_qat_positive_nothing_to_do():
     op1 = relay.qnn.op.quantize(
         relay.const(1.0), relay.const(12.0), relay.const(0), out_dtype="int32"
     )
-    op2 = relay.qnn.op.add(
+    op2 = relay.op.add(
         op0,
         op1,
-        relay.const(12.0),
-        relay.const(0),
-        relay.const(12.0),
-        relay.const(0),
-        relay.const(12.0),
-        relay.const(0),
     )
     expected_expr = relay.qnn.op.requantize(
         op2, relay.const(12.0), relay.const(0), relay.const(1.0), relay.const(0), out_dtype="int8"