def generate_random_number():
"""Generates a random number between 0-15.
Returns:
int: A random number between 0-15.
"""
# Allocate 4 qubits.
qubits = cirq.LineQubit.range(4)
# Set up circuit.
# Apply Hadamard gate to all qubits.
circuit = cirq.Circuit()
for qubit in qubits:
circuit.append(cirq.H(qubit))
# Measure all qubits.
for i in range(4):
circuit.append(cirq.measure(qubits[i], key=f'qubit{i}'))
# Initialise simulator and run circuit.
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1)
# Convert result as binary digits into integer.
qubit_results = []
for i in range(4):
qubit_results.append(result.data[f'qubit{i}'][0])
random_number = big_endian_bits_to_int(qubit_results)
# Return random number.
return random_number
def _tuple_of_big_endian_int(bit_groups: Iterable[Any]) -> Tuple[int, ...]:
"""Returns the big-endian integers specified by groups of bits.
Args:
bit_groups: Groups of descending bits, each specifying a big endian
integer with the 1s bit at the end.
Returns:
A tuple containing the integer for each group.
"""
return tuple(value.big_endian_bits_to_int(bits) for bits in bit_groups)
def data(self) -> pd.DataFrame:
if self._data is None:
# Convert to a DataFrame with columns as measurement keys, rows as
# repetitions and a big endian integer for individual measurements.
converted_dict = {}
for key, val in self._measurements.items():
converted_dict[key] = [
value.big_endian_bits_to_int(m_vals) for m_vals in val
]
self._data = pd.DataFrame(converted_dict)
return self._data
def data(self) -> pd.DataFrame:
if self._data is None:
# Convert to a DataFrame with columns as measurement keys, rows as
# repetitions and a big endian integer for individual measurements.
converted_dict = {}
for key, val in self._measurements.items():
converted_dict[key] = [value.big_endian_bits_to_int(m_vals) for m_vals in val]
# Note that when a numpy array is produced from this data frame,
# Pandas will try to use np.int64 as dtype, but will upgrade to
# object if any value is too large to fit.
self._data = pd.DataFrame(converted_dict, dtype=np.int64)
return self._data
def dataframe_from_measurements(
measurements: Mapping[str, np.ndarray]) -> pd.DataFrame:
"""Converts the given measurements to a pandas dataframe.
This can be used by subclasses as a default implementation for the data
property. Note that subclasses should typically memoize the result to
avoid recomputing.
"""
# Convert to a DataFrame with columns as measurement keys, rows as
# repetitions and a big endian integer for individual measurements.
converted_dict = {
key: [value.big_endian_bits_to_int(m_vals) for m_vals in val]
for key, val in measurements.items()
}
# Note that when a numpy array is produced from this data frame,
# Pandas will try to use np.int64 as dtype, but will upgrade to
# object if any value is too large to fit.
return pd.DataFrame(converted_dict, dtype=np.int64)
