# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Evaluate a quantum circuit."""
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
from mindspore import Tensor
from mindquantum.parameterresolver import ParameterResolver as PR
from mindquantum.nn import generate_evolution_operator
from mindquantum.utils import normalize
from mindquantum.utils import ket_string
from mindquantum.circuit import Circuit
def _generate_n_qubits_index(n_qubits):
out = []
for i in range(1 << n_qubits):
out.append(bin(i)[2:].zfill(n_qubits))
return out
[docs]class StateEvolution:
"""
Calculate the final state of a parameterized or non parameterized quantum circuit.
Args:
circuit (Circuit): The circuit that you want to do evolution.
Examples:
>>> from mindquantum.circuit import StateEvolution
>>> from mindquantum.circuit import qft
>>> print(StateEvolution(qft([0, 1])).final_state(ket=True))
0.5¦00⟩
0.5¦01⟩
0.5¦10⟩
0.5¦11⟩
"""
def __init__(self, circuit):
if not isinstance(circuit, Circuit):
raise TypeError(
f'Input circuit should be a quantum circuit, but get {type(circuit)}'
)
self.circuit = circuit
self.evol = generate_evolution_operator(self.circuit)
self.index = _generate_n_qubits_index(self.circuit.n_qubits)
[docs] def final_state(self, param=None, ket=False):
"""
Get the final state of the input quantum circuit.
Args:
param (Union[Tensor, numpy.ndarray, ParameterResolver, dict]): The
parameter for the parameterized quantum circuit. If None, the
quantum circuit should be a non parameterized quantum circuit.
Default: None.
ket (bool): Whether to print the final state in ket format. Default: False.
Returns:
numpy.ndarray, the final state in numpy array format.
"""
if param is None:
if self.circuit.para_name:
raise ValueError(
"Require a non parameterized quantum circuit, since not parameters specified."
)
return self.evol() if not ket else '\n'.join(
ket_string(self.evol()))
if isinstance(param, np.ndarray):
return self.evol(Tensor(param)) if not ket else '\n'.join(
ket_string(self.evol(Tensor(param))))
if isinstance(param, Tensor):
return self.evol(param) if not ket else '\n'.join(
ket_string(self.evol(param)))
if isinstance(param, (PR, dict)):
data = [param[i] for i in self.circuit.para_name]
data = Tensor(np.array(data).astype(np.float32))
return self.evol(data) if not ket else '\n'.join(
ket_string(self.evol(data)))
raise TypeError(
f"parameter requires a numpy array or a ParameterResolver or a dict, ut get {type(param)}"
)
[docs] def sampling(self, shots=1, param=None, show=False):
"""
Sampling the bit string based on the final state.
Args:
shots (int): How many samples you want to get. Default: 1.
param (Union[Tensor, numpy.ndarray, ParameterResolver, dict]): The
parameter for the parameterized quantum circuit. If None, the
quantum circuit should be a non parameterized quantum circuit.
Default: None.
show (bool): Whether to show the sampling result in bar plot. Default: False.
Returns:
dict, a dict with key as bit string and value as number of samples.
Examples:
>>> from mindquantum.circuit import StateEvolution
>>> from mindquantum.circuit import qft
>>> import numpy as np
>>> np.random.seed(42)
>>> StateEvolution(qft([0, 1])).sampling(100)
{'00': 29, '01': 24, '10': 23, '11': 24}
"""
final_state = self.final_state(param)
amps = normalize(np.abs(final_state)**2)**2
sampling = Counter(np.random.choice(self.index, p=amps, size=shots))
result = dict(zip(self.index, [0] * len(self.index)))
result.update(sampling)
if show:
plt.bar(result.keys(), result.values())
if self.circuit.n_qubits > 2:
plt.xticks(rotation=45)
plt.show()
return result