diff --git a/test/quantization/test_quant_primitives.py b/test/quantization/test_quant_primitives.py
index 4bc3236759..0c1050a92d 100644
--- a/test/quantization/test_quant_primitives.py
+++ b/test/quantization/test_quant_primitives.py
@@ -15,6 +15,7 @@
     MappingType,
     ZeroPointDomain,
     _choose_qparams_affine_tinygemm,
+    _choose_qparams_and_quantize_scale_only_hqq,
     _choose_qparams_and_quantize_scale_only_sinq,
     _choose_scale_float8,
     _fake_quantize_affine,
@@ -29,6 +30,7 @@
 # TODO: remove test for utils?
 from torchao.quantization.utils import (
     _quantize_activation_per_token_absmax,
+    compute_error,
     get_block_size,
     get_group_qparams_symmetric,
     groupwise_affine_dequantize_tensor_from_qparams,
@@ -863,6 +865,42 @@ def test_choose_qparams_and_quantize_scale_only_sinq(self):
         ).reshape(input.shape)
         self.assertFalse(torch.isnan(reconstructed).any())
 
+    def test_choose_qparams_and_quantize_scale_only_hqq(self):
+        """Test HQQ quantization produces valid outputs with correct shapes and ranges."""
+        torch.manual_seed(self.SEED)
+        input = torch.randn(128, 256, dtype=torch.float32)
+        block_size = [1, 64]
+        qmin = -(2 ** (4 - 1))
+        qmax = 2 ** (4 - 1) - 1
+
+        qdata, scale = _choose_qparams_and_quantize_scale_only_hqq(
+            input,
+            block_size=block_size,
+            qmin=qmin,
+            qmax=qmax,
+            iters=20,
+        )
+
+        # Check quantized data shape and dtype
+        self.assertEqual(qdata.dtype, torch.int32)
+        self.assertEqual(qdata.shape, input.shape)
+        self.assertTrue((qdata >= qmin).all() and (qdata <= qmax).all())
+
+        # Check scale shape and values
+        num_groups = input.shape[1] // block_size[1]
+        self.assertEqual(scale.shape, (input.shape[0], num_groups))
+        self.assertEqual(scale.dtype, input.dtype)
+        self.assertTrue((scale > 0).all())
+
+        # Test reconstruction is possible
+        scale_expanded = scale.repeat_interleave(block_size[1], dim=1)
+        reconstructed = qdata.to(input.dtype) * scale_expanded
+        self.assertFalse(torch.isnan(reconstructed).any())
+        self.assertEqual(reconstructed.shape, input.shape)
+
+        error = compute_error(input, reconstructed)
+        self.assertLess(error, 25)
+
     def test_float8_blockwise_scaling(self):
         M, K = 512, 1024
         hp_tensor = torch.randn(M, K, dtype=torch.float)