def iplot_state(quantum_state, method='city', figsize=None):
"""Plot the quantum state.
Args:
quantum_state (ndarray): statevector or density matrix
representation of a quantum state.
method (str): Plotting method to use.
figsize (tuple): Figure size in pixels.
Raises:
VisualizationError: if the input is not a statevector or density
matrix, or if the state is not an multi-qubit quantum state.
"""
warnings.warn(
"iplot_state is deprecated, and will be removed in \
the 0.9 release. Use the iplot_state_ * functions \
instead.", DeprecationWarning)
rho = _validate_input_state(quantum_state)
if method == "city":
iplot_state_city(rho, figsize=figsize)
elif method == "paulivec":
iplot_state_paulivec(rho, figsize=figsize)
elif method == "qsphere":
iplot_state_qsphere(rho, figsize=figsize)
elif method == "bloch":
iplot_bloch_multivector(rho, figsize=figsize)
elif method == "hinton":
iplot_state_hinton(rho, figsize=figsize)
else:
raise VisualizationError('Invalid plot state method.')
def bit_string_index(text):
"""Return the index of a string of 0s and 1s."""
n = len(text)
k = text.count("1")
if text.count("0") != n - k:
raise VisualizationError("s must be a string of 0 and 1")
ones = [pos for pos, char in enumerate(text) if char == "1"]
return lex_index(n, k, ones)
def _validate_input_state(quantum_state):
"""Validates the input to state visualization functions.
Args:
quantum_state (ndarray): Input state / density matrix.
Returns:
rho: A 2d numpy array for the density matrix.
Raises:
VisualizationError: Invalid input.
"""
rho = np.asarray(quantum_state)
if rho.ndim == 1:
rho = np.outer(rho, np.conj(rho))
# Check the shape of the input is a square matrix
shape = np.shape(rho)
if len(shape) != 2 or shape[0] != shape[1]:
raise VisualizationError("Input is not a valid quantum state.")
# Check state is an n-qubit state
num = int(np.log2(rho.shape[0]))
if 2 ** num != rho.shape[0]:
raise VisualizationError("Input is not a multi-qubit quantum state.")
return rho
def lex_index(n, k, lst):
"""Return the lex index of a combination..
Args:
n (int): the total number of options .
k (int): The number of elements.
lst (list): list
Returns:
int: returns int index for lex order
Raises:
VisualizationError: if length of list is not equal to k
"""
if len(lst) != k:
raise VisualizationError("list should have length k")
comb = list(map(lambda x: n - 1 - x, lst))
dualm = sum([n_choose_k(comb[k - 1 - i], i + 1) for i in range(k)])
return int(dualm)