apply comments on py

pennylane_lightning/lightning_tensor/_tensornet.py

@@ -35,14 +35,14 @@ def svd_split(Mat, site_shape, max_bond_dim):
     """SVD decomposition of a matrix via numpy linalg. Note that this function is to be moved to the C++ layer."""
     # TODO: Check if cutensornet allows us to remove all zero (or < tol) singular values and the respective rows and columns of U and Vd
     U, S, Vd = np.linalg.svd(Mat, full_matrices=False)
-    U = U @ np.diag(S)  # Append singular values to U
+    U = U * S  # Append singular values to U
     bonds = len(S)
 
-    Vd = Vd.reshape(tuple([bonds] + site_shape + [-1]))
-    U = U.reshape(tuple([-1] + site_shape + [bonds]))
+    Vd = Vd.reshape([bonds] + site_shape + [-1])
+    U = U.reshape([-1] + site_shape + [bonds])
 
     # keep only chi bonds
-    chi = np.min([bonds, max_bond_dim])
+    chi = min([bonds, max_bond_dim])
     U, Vd = U[..., :chi], Vd[:chi]
     return U, Vd
 
@@ -57,7 +57,7 @@ def decompose_dense(psi, n_wires, site_shape, max_bond_dim):
         psi, site_shape, max_bond_dim
     )  # psi [site_len, -1] -> U [site_len, mu] Vd [mu, (2x2x2x..)]
 
-    Ms[0] = U.reshape(tuple(site_shape + [-1]))
+    Ms[0] = U.reshape(site_shape + [-1])
     bondL = Vd.shape[0]
     psi = Vd
 
@@ -71,7 +71,7 @@ def decompose_dense(psi, n_wires, site_shape, max_bond_dim):
         psi = Vd
         bondL = Vd.shape[0]
 
-    Ms[-1] = Vd.reshape(tuple([-1] + site_shape))
+    Ms[-1] = Vd.reshape([-1] + site_shape)
 
     return Ms
 

tests/lightning_tensor/test_tensornet_class.py

@@ -101,7 +101,9 @@ def test_gate_matrix_decompose():
     gate = scipy.linalg.expm(1j * hermitian)
     original_gate = gate.copy()  # for later to double check
 
-    mpos, sorted_wired = gate_matrix_decompose(gate, wires, np.complex128)
+    max_mpo_bond_dim = 2**len(wires)
+
+    mpos, sorted_wired = gate_matrix_decompose(gate, wires, max_mpo_bond_dim, np.complex128)
 
     # restore the C-ordering of the matrices
     mpo0 = np.transpose(mpos[0], axes=(2, 1, 0))