def construct_unitary(self) -> np.ndarray:
return np.diag([1, 1, 1, -1]).astype(DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> np.ndarray:
return np.diag([1, 1, 1, np.exp(1j * self.parameter)
]).astype(DTYPES.get('NPTYPECPX'))
def reprepare(self):
n = len(self.result)
result = sum(2**(n - i - 1) * r for i, r in enumerate(self.result))
self.result_tensor = tf.cast(result, dtype=DTYPES.get('DTYPEINT'))
def _prepare(self):
with tf.device(self.device):
self.matrix = tf.constant(np.exp(1j * self.parameter),
dtype=DTYPES.get('DTYPECPX'))
def _prepare(self):
with tf.device(self.device):
self.matrix = tf.constant(self.construct_unitary(),
dtype=DTYPES.get('DTYPECPX'))
def construct_unitary(self) -> tf.Tensor:
t = tf.cast(self.parameters, dtype=DTYPES.get('DTYPECPX'))
phase = tf.exp(1j * t / 2.0)[tf.newaxis]
diag = tf.concat([tf.math.conj(phase), phase], axis=0)
return tf.linalg.diag(diag)
def construct_unitary(self) -> np.ndarray:
return (np.array([[1, 1], [1, -1]], dtype=DTYPES.get('NPTYPECPX')) /
np.sqrt(2))
def construct_unitary(self) -> np.ndarray:
matrix = np.eye(8, dtype=DTYPES.get('NPTYPECPX'))
matrix[-2, -2], matrix[-2, -1] = 0, 1
matrix[-1, -2], matrix[-1, -1] = 1, 0
return matrix
def state_vector_call(self, state: tf.Tensor) -> tf.Tensor:
shape = tuple(state.shape)
_state = np.array(self.coefficients).reshape(shape)
return tf.convert_to_tensor(_state, dtype=DTYPES.get("DTYPECPX"))
def construct_unitary(self) -> np.ndarray:
matrix = np.diag(
[1 - self.preset1, self.exp_t2, self.exp_t2, 1 - self.preset0])
matrix[0, -1] = self.preset1
matrix[-1, 0] = self.preset0
return matrix.astype(DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> np.ndarray:
phi, lam = self.parameters
eplus = np.exp(1j * (phi + lam) / 2.0)
eminus = np.exp(1j * (phi - lam) / 2.0)
return np.array([[eplus.conj(), -eminus.conj()], [eminus, eplus]],
dtype=DTYPES.get('NPTYPECPX')) / np.sqrt(2)
def construct_unitary(self) -> np.ndarray:
theta = self.parameters
cos, isin = np.cos(theta / 2.0), -1j * np.sin(theta / 2.0)
return np.array([[cos, isin], [isin, cos]],
dtype=DTYPES.get('NPTYPECPX'))
def density_matrix_call(self, state: tf.Tensor) -> tf.Tensor:
shape = 2 * self.nqubits * (2, )
x = tf.einsum(self.traceout, tf.reshape(state, shape))
return tf.cast(x, dtype=DTYPES.get('DTYPE'))
def construct_unitary(self) -> np.ndarray:
return np.array(
[[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]],
dtype=DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> np.ndarray:
return np.array([[0, -1j], [1j, 0]], dtype=DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> np.ndarray:
unitary, phi = self.parameter
matrix = np.eye(4, dtype=DTYPES.get('NPTYPECPX'))
matrix[1:3, 1:3] = np.reshape(unitary, (2, 2))
matrix[3, 3] = np.exp(-1j * phi)
return matrix
def construct_unitary(self) -> np.ndarray:
dim = 2**len(self.target_qubits)
return np.eye(dim, dtype=DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> np.ndarray:
unitary = self.parameter
if isinstance(unitary, np.ndarray):
return unitary.astype(DTYPES.get('NPTYPECPX'))
if isinstance(unitary, tf.Tensor):
return tf.identity(tf.cast(unitary, dtype=DTYPES.get('DTYPECPX')))
def construct_unitary(self) -> np.ndarray:
cos, sin = np.cos(self.parameter / 2.0), np.sin(self.parameter / 2.0)
return np.array([[cos, -sin], [sin, cos]],
dtype=DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> tf.Tensor:
t = tf.cast(self.parameters, dtype=DTYPES.get('DTYPECPX'))
return tf.cos(t / 2.0) * matrices.I - 1j * tf.sin(t / 2.0) * matrices.Y
def construct_unitary(self) -> np.ndarray:
phase = np.exp(1j * self.parameter / 2.0)
return np.diag([phase.conj(), phase]).astype(DTYPES.get('NPTYPECPX'))
def construct_unitary(self) -> tf.Tensor:
t = tf.cast(self.parameters, dtype=DTYPES.get('DTYPECPX'))
phase = tf.exp(1j * t)
return tf.linalg.diag([1, phase])
def ground_state():
n = K.cast(2 ** nqubits, dtype=DTYPES.get('DTYPEINT'))
state = K.ones(n, dtype=DTYPES.get('DTYPECPX'))
return state / K.math.sqrt(K.cast(n, dtype=state.dtype))
