yehfelareborn commented on code in PR #1179:
URL: https://github.com/apache/mahout/pull/1179#discussion_r2941634415
##########
testing/qdp/test_numpy.py:
##########
@@ -147,6 +146,255 @@ def test_encode_numpy_encoding_methods(encoding_method):
_verify_tensor(tensor, (1, sample_size))
+# ---------------------------------------------------------------------------
+# Angle encoding tests
+# ---------------------------------------------------------------------------
+
+
+@requires_qdp
[email protected]
[email protected]("num_qubits", [1, 2, 3, 4])
+def test_encode_numpy_angle_encoding_1d(num_qubits):
+ """Angle encoding: 1D array with num_qubits angles (single sample)"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ # Angle encoding expects exactly num_qubits values: one angle per qubit
+ angles = np.random.uniform(0, 2 * np.pi,
size=num_qubits).astype(np.float64)
+
+ qtensor = engine.encode(angles, num_qubits, encoding_method="angle")
+ tensor = torch.from_dlpack(qtensor)
+
+ _verify_tensor(tensor, (1, 2**num_qubits), check_normalization=True)
+
+
+@requires_qdp
[email protected]
[email protected](("num_samples", "num_qubits"), [(5, 2), (10, 3), (1,
4)])
+def test_encode_numpy_angle_encoding_2d(num_samples, num_qubits):
+ """Angle encoding: 2D array of shape (num_samples, num_qubits)"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ angles = np.random.uniform(0, 2 * np.pi, size=(num_samples,
num_qubits)).astype(
+ np.float64
+ )
+
+ qtensor = engine.encode(angles, num_qubits, encoding_method="angle")
+ tensor = torch.from_dlpack(qtensor)
+
+ _verify_tensor(tensor, (num_samples, 2**num_qubits),
check_normalization=True)
+
+
+@requires_qdp
[email protected]
+def test_encode_numpy_angle_encoding_from_file():
+ """Angle encoding: load angles from .npy file"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ num_qubits = 3
+ num_samples = 8
+ angles = np.random.uniform(0, 2 * np.pi, size=(num_samples,
num_qubits)).astype(
+ np.float64
+ )
+
+ with tempfile.NamedTemporaryFile(suffix=".npy", delete=False) as f:
+ npy_path = f.name
+ try:
+ np.save(npy_path, angles)
+ qtensor = engine.encode(npy_path, num_qubits, encoding_method="angle")
+ tensor = torch.from_dlpack(qtensor)
+ _verify_tensor(tensor, (num_samples, 2**num_qubits),
check_normalization=True)
+ finally:
+ if os.path.exists(npy_path):
+ os.remove(npy_path)
+
+
+@requires_qdp
[email protected]
+def test_encode_numpy_angle_encoding_wrong_sample_size():
+ """Angle encoding: wrong sample_size raises an error"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ num_qubits = 3
+ # Pass 2^num_qubits values instead of num_qubits values
+ wrong_data = np.ones(2**num_qubits, dtype=np.float64)
+
+ with pytest.raises((RuntimeError, ValueError)):
+ engine.encode(wrong_data, num_qubits, encoding_method="angle")
+
+
+# ---------------------------------------------------------------------------
+# Basis encoding tests
+# ---------------------------------------------------------------------------
+
+
+@requires_qdp
[email protected]
[email protected]("num_qubits", [1, 2, 3, 4])
+def test_encode_numpy_basis_encoding_1d(num_qubits):
+ """Basis encoding: 1D array with a single index (single sample)"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ # Basis encoding expects sample_size=1: one integer index per sample
+ index = np.array([float(2**num_qubits - 1)], dtype=np.float64)
+
+ qtensor = engine.encode(index, num_qubits, encoding_method="basis")
+ tensor = torch.from_dlpack(qtensor)
+
+ _verify_tensor(tensor, (1, 2**num_qubits), check_normalization=True)
+
+
+@requires_qdp
[email protected]
[email protected](("num_samples", "num_qubits"), [(5, 2), (10, 3), (1,
4)])
+def test_encode_numpy_basis_encoding_2d(num_samples, num_qubits):
+ """Basis encoding: 2D array of shape (num_samples, 1) with integer
indices"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ max_index = 2**num_qubits
+ indices = np.random.randint(0, max_index, size=(num_samples,
1)).astype(np.float64)
+
+ qtensor = engine.encode(indices, num_qubits, encoding_method="basis")
+ tensor = torch.from_dlpack(qtensor)
+
+ _verify_tensor(tensor, (num_samples, 2**num_qubits),
check_normalization=True)
+
+
+@requires_qdp
[email protected]
+def test_encode_numpy_basis_encoding_from_file():
+ """Basis encoding: load indices from .npy file"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0)
+ num_qubits = 3
+ num_samples = 6
+ indices = np.random.randint(0, 2**num_qubits, size=(num_samples,
1)).astype(
+ np.float64
+ )
+
+ with tempfile.NamedTemporaryFile(suffix=".npy", delete=False) as f:
+ npy_path = f.name
+ try:
+ np.save(npy_path, indices)
+ qtensor = engine.encode(npy_path, num_qubits, encoding_method="basis")
+ tensor = torch.from_dlpack(qtensor)
+ _verify_tensor(tensor, (num_samples, 2**num_qubits),
check_normalization=True)
+ finally:
+ if os.path.exists(npy_path):
+ os.remove(npy_path)
+
+
+@requires_qdp
[email protected]
[email protected](
+ ("basis_index", "num_qubits"),
+ [
+ (0, 2), # |00⟩: amplitude 1 at index 0
+ (1, 2), # |01⟩: amplitude 1 at index 1
+ (3, 2), # |11⟩: amplitude 1 at index 3
+ (0, 3), # |000⟩: amplitude 1 at index 0
+ (13, 4), # |1101⟩: amplitude 1 at index 13
+ ],
+)
+def test_encode_numpy_basis_state_correctness(basis_index, num_qubits):
+ """Basis encoding: verify the encoded state is exactly |basis_index⟩"""
+ from _qdp import QdpEngine
+
+ pytest.importorskip("torch")
+ if not torch.cuda.is_available():
+ pytest.skip("GPU required for QdpEngine")
+
+ engine = QdpEngine(device_id=0, precision="float64")
+ data = np.array([[float(basis_index)]], dtype=np.float64)
+
+ qtensor = engine.encode(data, num_qubits, encoding_method="basis")
+ tensor = torch.from_dlpack(qtensor)
+
+ # The encoded state should be a one-hot complex vector with |amplitude|²=1
+ # at basis_index and 0 elsewhere
+ probs = tensor.abs().pow(2).squeeze(0) # shape: (2^num_qubits,)
+ expected = torch.zeros(2**num_qubits, dtype=probs.dtype,
device=probs.device)
+ expected[basis_index] = 1.0
+
+ assert torch.allclose(probs, expected, atol=1e-6), (
+ f"Expected |{basis_index}⟩ but got probabilities
{probs.cpu().tolist()}"
+ )
+
+
+@requires_qdp
[email protected]
[email protected](
+ ("bad_data", "description"),
+ [
+ (np.array([[-1.0]], dtype=np.float64), "negative index"),
+ (np.array([[0.5]], dtype=np.float64), "non-integer index"),
+ ],
+)
+def test_encode_numpy_basis_encoding_invalid_index(bad_data, description):
Review Comment:
Hi, i removed the description part and replaced it with pytest.param, thanks
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