import pennylane as qml
import pennylane.numpy as qnp
import numpy as np
from pennylane import math
import itertools
import copy
import json
[docs]def unitary_matrix(circuit, num_wires, *circ_args, **circ_kwargs):
"""Constructs the unitary matrix representation of a quantum
circuit in the computational basis.
:param circuit: A quantum function.
:type circuit: Function
:param num_wires: The number of wires needed by ``circuit``.
:type num_wires: Int
:param circ_args: Passthrough arguments for ``circuit``.
:type circ_args: Positional Arguments
:param circ_kwargs: Passthrough keyword arguments for ``circuit``.
:type circ_kwargs: keyword Arguments
:returns: A unitary matrix representing the provided ``circuit``.
:rtype: Numpy Array
"""
state_vec = state_vec_fn(circuit, num_wires)
bitstrings = [np.array(bitstring) for bitstring in itertools.product([0, 1], repeat=num_wires)]
unitary = [
state_vec(*circ_args, basis_state=bitstring, **circ_kwargs).numpy()
for bitstring in bitstrings
]
return np.array(unitary).T
[docs]def state_vec_fn(circuit, num_wires):
"""Constructs a function ``state_vec(*circ_args, basis_state=[0,...,0], **circ_kwargs)``
that returns the state vector representation of the output of ``circuit`` where the input
to the circuit is specified in the computational basis by ``basis_state``.
:param circuit: A quantum function.
:type circuit: Function
:param num_wires: The number of wires to evaluate ``circuit`` on.
:type num_wires: Int
:returns: A vector representing the pure quantum state output from ``circuit(*circ_args, **circ_kwargs)``
when the computational ``basis_state`` is provided as input.
:rtype: np.array
"""
dev_wires = range(num_wires)
dev = qml.device("default.qubit", wires=dev_wires)
zero_state = np.array([0] * len(dev_wires))
@qml.qnode(dev)
def state_vec(*circ_args, basis_state=zero_state, **circ_kwargs):
qml.BasisState(basis_state, wires=dev_wires)
circuit(*circ_args, **circ_kwargs)
return qml.state()
return state_vec
[docs]def density_mat_fn(circuit, num_wires):
"""Constructs a function that returns the density matrix of the specified ``circuit``.
:param circuit: A quantum function.
:type circuit: Function
:param num_wires: The number of wires to evaluate ``circuit`` on.
:type num_wires: Int
:returns: A function ``density_mat(wires_out, *circ_args, basis_state=[0,...,0], **circ_kwargs)`` that returns
the density matrix representing the quantum state on ``wires_out`` for the initialized ``basis_state`` where
the quantum circuit is called as ``circuit(*circ_args, **circ_kwargs)``.
:rtype: np.array
"""
dev_wires = range(num_wires)
dev = qml.device("default.qubit", wires=dev_wires)
zero_state = np.array([0] * len(dev_wires))
@qml.qnode(dev)
def density_mat(wires_out, *circ_args, basis_state=zero_state, **circ_kwargs):
qml.BasisState(basis_state, wires=dev_wires)
circuit(*circ_args, **circ_kwargs)
return qml.density_matrix(wires=wires_out)
return density_mat
[docs]def write_optimization_json(opt_dict, filename):
"""Writes the optimization dictionary to a JSON file.
:param opt_dict: The dictionary returned by a network optimization.
:type opt_dict: dict
:param filename: The name of the JSON file to be written. Note that ``.json`` extension is automatically added.
:type filename: string
:returns: ``None``
"""
opt_dict_json = copy.deepcopy(opt_dict)
opt_dict_json["opt_score"] = float(opt_dict_json["opt_score"])
opt_dict_json["scores"] = [float(score) for score in opt_dict_json["scores"]]
opt_dict_json["opt_settings"] = [float(setting) for setting in opt_dict_json["opt_settings"]]
opt_dict_json["settings_history"] = [
[float(setting) for setting in settings] for settings in opt_dict_json["settings_history"]
]
with open(filename + ".json", "w") as file:
file.write(json.dumps(opt_dict_json, indent=2))
[docs]def read_optimization_json(filepath):
"""Reads data from an optimization JSON created via ``write_optimization_json``.
:param filepath: The path to the JSON file. Note this string must contain the ``.json`` extension.
:type filepath: string
:returns: The optimization dictionary read from the file.
:rtype: dict
"""
with open(filepath) as file:
opt_dict = json.load(file)
return opt_dict
[docs]def mixed_base_num(n, base_digits):
"""Converts a base-10 number ``n`` into a mixed base number with digit
values described by the ``base_digits`` array.
:param n: A base-10 number
:type n: int
:param base_digits: A list of integers representing the largest value for each
digit in the mixed base number
:type base_digits: list[int]
:returns: A list of integers representing the mixed base number.
:rtype: list[int]
"""
mixed_base_digits = []
n_tmp = n
for i in range(len(base_digits)):
place = int(math.prod(base_digits[i + 1 :]))
mixed_base_digits += [n_tmp // place]
n_tmp = n_tmp % place
return mixed_base_digits
def ragged_reshape(input_list, list_dims):
"""Takes a 1D list ``input_list`` and breaks it into smaller lists having lengths specified by the
elements of ``list_dims``.
:param input_list: The list to reshape.
:type input_list: list
:param list_dims: The length of each element in the output list
:type list_dims: list[int]
:returns: The original list reshaped as a list of lists where each element has length specified
by ``list_dims``.
:rtype: list[list]
:raises ValueError: If `len(input_list) != sum(list_dims)` because list cannot be repartitioned.
"""
if math.sum(list_dims) != len(input_list):
raise ValueError("`len(input_list)` must match the sum of `list_dims`.")
output_list = []
start_id = 0
for i, num_nodes in enumerate(list_dims):
output_list += [[]]
end_id = start_id + num_nodes
output_list[i] += input_list[start_id:end_id]
start_id = end_id
return output_list