radixnew.py

radixnew.py

@@ -0,0 +1,115 @@
+# Define the Radixes class
+
+class Radixes:
+    def __init__(self, radixes: Union[int, Sequence[int], None], dim: int) -> None:
+        if isinstance(radixes, int):
+            self.radixes = (radixes,) * int(np.round(np.log2(dim)))
+        elif isinstance(radixes, Sequence):
+            if len(radixes) != dim:
+                raise ValueError(
+                    'Length of radixes sequence does not match dimension of input matrix.'
+                )
+            self.radixes = tuple(radixes)
+        else:
+            # If radixes is not provided, infer from the dimension of the input matrix
+            if dim & (dim - 1) == 0:
+                self.radixes = tuple([2] * int(np.round(np.log2(dim))))
+            elif 3 ** int(np.round(np.log(dim) / np.log(3))) == dim:
+                self.radixes = [3] * int(np.round(np.log(dim) / np.log(3)))
+            else:
+                raise RuntimeError(
+                    'Unable to determine radixes for UnitaryMatrix with dim %d.' % dim,
+                )
+
+# Update the __init__ method in the UnitaryMatrix class
+
+def __init__(
+    self,
+    input: UnitaryLike,
+    radixes: RadixesLike = None,
+    check_arguments: bool = True,
+) -> None:
+    """
+    Constructs a `UnitaryMatrix` from the supplied unitary matrix.
+
+    Args:
+        input (UnitaryLike): The unitary matrix input.
+
+        radixes (RadixesLike): An instance of Radixes class or a sequence of radices,
+            specifying the base, number of orthogonal states, for each qudit in the system.
+
+        check_arguments (bool): If true, check arguments for type
+            and value errors.
+
+    Raises:
+        ValueError: If `input` is not unitary.
+
+        ValueError: If the dimension of `input` does not match the
+            expected dimension from `radixes`.
+
+        RuntimeError: If `radixes` is not specified and the
+            constructor cannot infer it.
+    """
+
+    # Copy constructor
+    if isinstance(input, UnitaryMatrix):
+        self._utry = input.numpy
+        self._radixes = input.radixes
+        self._dim = input.dim
+        return
+
+    if check_arguments and not is_square_matrix(input):
+        raise TypeError(f'Expected square matrix, got {type(input)}.')
+
+    if check_arguments and not UnitaryMatrix.is_unitary(input):
+        raise ValueError('Input failed unitary condition.')
+
+    dim = len(input)
+    radixes = Radixes(radixes, dim)
+
+    if check_arguments and not is_valid_radixes(radixes.radixes):
+        raise TypeError('Invalid qudit radixes.')
+
+    if check_arguments and np.prod(radixes.radixes) != dim:
+        raise ValueError('Qudit radixes mismatch with dimension.')
+
+    self._utry = np.array(input, dtype=np.complex128)
+    self._dim = dim
+    self._radixes = radixes.radixes
+
+import unittest
+
+class UnitaryMatrixTest(unittest.TestCase):
+
+    def test_radixes_single_radix(self):
+        # Test case where a single radix is provided
+        utry = np.array([[0, 1], [1, 0]])
+        unitary = UnitaryMatrix(utry, radixes=3)
+        self.assertEqual(unitary.radixes, (3, 3))
+
+    def test_radixes_sequence(self):
+        # Test case where a sequence of radices is provided
+        utry = np.array([[0, 1], [1, 0]])
+        unitary = UnitaryMatrix(utry, radixes=[2, 3])
+        self.assertEqual(unitary.radixes, (2, 3))
+
+    def test_radixes_infer(self):
+        # Test case where radixes are inferred from the dimension of the input matrix
+        utry = np.array([[0, 1], [1, 0], [0, 0], [0, 1]])
+        # Dimension of input matrix is 4, which is a power of 2
+        unitary = UnitaryMatrix(utry)
+        # Expected radixes should be [2, 2]
+        self.assertEqual(unitary.radixes, (2, 2))
+
+    def test_radixes_infer_non_power_of_2(self):
+        # Test case where dimension of input matrix is not a power of 2
+        utry = np.array([[0, 1], [1, 0], [0, 0], [0, 1], [1, 0]])
+        # Dimension of input matrix is 5, which is not a power of 2
+        unitary = UnitaryMatrix(utry)
+        # Expected radixes should be [5]
+        self.assertEqual(unitary.radixes, (5,))
+
+    def test_radixes_infer_non_power_of_3(self):
+        # Test case where dimension of input matrix is not a power of 3
+        utry = np.array([[0, 1], [1, 0], [0, 0], [0, 1], [1, 0], [0, 0]])
+        # Dimension of input matrix is 6, which is not