【代码贡献】修复 QmRMR 星号导入报错、8 个无法运行的示例及文档中的过时接口

pyqpanda-algorithm/example/QAlgBase/QmRMR/testeg_QmRMR.py

@@ -1,9 +1,7 @@
-import sympy as sp
 import numpy as np
-import pyqpanda as pq
 import warnings
 
-from pyqpanda_alg.QFinance.QmRMR.all_code import plot_bar, plot_loss, Feature_Selection
+from pyqpanda_alg.QmRMR.QmRMR_core import plot_bar, plot_loss, Feature_Selection
 import os
 import matplotlib.pyplot as plt
 warnings.simplefilter("ignore")

pyqpanda-algorithm/example/QAlgBase/newtest.py

@@ -1,31 +1,18 @@
-import pyqpanda as pq
 import numpy as np
+from pyqpanda3.core import CPUQVM, QCircuit, QProg, RY, X
 
-#
-# def f(a=1, b=2):
-#     return a+b
-#
-# def f1(a):
-#     return f(a, b=2)
-#
-# def g(func):
-#     res = func(3)
-#     return res
-#
-#
-# print(g(f1))
 
 def create_cir(qlist):
-    cir = pq.QCircuit()
-    cir << pq.RY(qlist[0], np.pi / 3) << pq.X(qlist[1]).control(qlist[0])
+    cir = QCircuit()
+    cir << RY(qlist[0], np.pi / 3) << X(qlist[1]).control(qlist[0])
     return cir
 
 
-m = pq.CPUQVM()
-m.initQVM()
-q_state = m.qAlloc_many(2)
+if __name__ == '__main__':
+    m = CPUQVM()
+    q_state = QProg(2).qubits()
 
-prog = pq.QProg()
-prog << create_cir(q_state)
+    prog = QProg()
+    prog << create_cir(q_state)
 
-print(prog)
+    print(prog)

pyqpanda-algorithm/example/QAlgBase/testeg_QUBO.py

@@ -1,7 +1,7 @@
-from pyqpanda_alg.QFinance import QUBO
 import sympy as sp
-import numpy as np
-import pyqpanda as pq
+from pyqpanda3.core import QProg
+
+from pyqpanda_alg import QUBO
 
 
 if __name__ == '__main__':
@@ -11,10 +11,9 @@
     n_key, n_res = test0.query_qnumber()
     print(n_key, n_res)
 
-    m = pq.CPUQVM()
-    m.initQVM()
-    q_key = m.qAlloc_many(n_key)
-    q_res = m.qAlloc_many(n_res)
+    q_all = QProg(n_key + n_res).qubits()
+    q_key = q_all[:n_key]
+    q_res = q_all[n_key:]
 
     print(test0.cir(q_key, q_res))
 
@@ -33,5 +32,5 @@
     # find the minimum function value using QAOA
     test2 = QUBO.QUBO_QAOA(function)
     res2 = test2.run(layer=5, optimizer='SLSQP',
-                     optimizer_option={'options':{'eps':1e-3}})
+                     optimizer_option={'options': {'eps': 1e-3}})
     print('result of QAOA: ', res2)

pyqpanda-algorithm/example/QAlgBase/testeg_QmRMR.py

@@ -4,7 +4,7 @@
 
 matplotlib.use('Agg')  # 使用非交互式后端
 import matplotlib.pyplot as plt
-from pyqpanda_alg.QmRMR import all_code
+from pyqpanda_alg.QmRMR import QmRMR_core as all_code
 import warnings
 import os
 

pyqpanda-algorithm/example/QAlgBase/testeg_class_qsvr.py

@@ -1,13 +1,12 @@
-from pyqpanda_alg.QFinance import class_qsvr
-import sympy as sp
 import numpy as np
-import pyqpanda as pq
+
+from pyqpanda_alg.QSVR import Quantum_SVR
+
 
 if __name__ == '__main__':
-
     n_samples = 100
     n_features = 2
     X = np.random.rand(n_samples, n_features) * 10
     y = (2 * np.sin(X[:, 0]) +
          1.5 * np.cos(X[:, 1]))
-    class_qsvr.Quantum_SVR(X, y).show_res()
+    Quantum_SVR(X, y).show_res()

pyqpanda-algorithm/example/QAlgBase/testeg_comparator.py

@@ -1,44 +1,48 @@
-from pyqpanda_alg.QFinance import comparator
-import pyqpanda as pq
+from pyqpanda3.core import CPUQVM, QCircuit, QProg, H, X
+
+from pyqpanda_alg import QCmp
 
 
 if __name__ == '__main__':
     # 整数比较
     value = 3
-    m = pq.CPUQVM()
-    m.initQVM()
-    q_state = m.qAlloc_many(2)
-    q_anc_cmp = m.qAlloc_many(2)
-    prog = pq.QProg()
-    prog << pq.H(q_state)
-    cir = comparator.int_comparator(value, q_state, q_anc_cmp, function='g', reuse=True)
+    m = CPUQVM()
+    q_state = [0, 1]
+    q_anc_cmp = [2, 3]
+    prog = QProg()
+    cir = QCircuit()
+    for q in q_state:
+        cir << H(q)
+    cir << QCmp.int_comparator(value, q_state, q_anc_cmp, function='g', reuse=True)
     prog << cir
-    res = m.prob_run_dict(prog, [q_anc_cmp[-1]])
-    print(res)
+    m.run(prog, 1000)
+    print(m.result().get_prob_dict([q_anc_cmp[-1]]))
 
     # 插值方法
     value = 3.3
-    m = pq.CPUQVM()
-    m.initQVM()
-    q_state = m.qAlloc_many(3)
-    q_anc_cmp = m.qAlloc_many(3)
-    prog = pq.QProg()
-    prog << pq.X(q_state[:2])
-    cir = comparator.interpolation_comparator(value, q_state, q_anc_cmp, function='g', reuse=True)
+    m = CPUQVM()
+    q_state = [0, 1, 2]
+    q_anc_cmp = [3, 4, 5]
+    prog = QProg()
+    cir = QCircuit()
+    for q in q_state[:2]:
+        cir << X(q)
+    cir << QCmp.interpolation_comparator(value, q_state, q_anc_cmp, function='g', reuse=True)
     prog << cir
-    res = m.prob_run_dict(prog, [q_anc_cmp[-1]])
-    print(res)
+    m.run(prog, 1000)
+    print(m.result().get_prob_dict([q_anc_cmp[-1]]))
 
     # 两个态比较，示例中叠加态的0，1，2，3有0.5的概率大于态1
-    m = pq.CPUQVM()
-    m.initQVM()
-    q_state_1 = m.qAlloc_many(2)
-    q_state_2 = m.qAlloc_many(2)
-    q_anc_cmp = m.qAlloc_many(2)
-    prog = pq.QProg()
-    prog << pq.H(q_state_1)
-    prog << pq.X(q_state_2[0])
-    cir = comparator.qubit_comparator(q_state_1, q_state_2, q_anc_cmp, function='g')
+    m = CPUQVM()
+    q_state_1 = [0, 1]
+    q_state_2 = [2, 3]
+    q_anc_cmp = [4, 5]
+    prog = QProg()
+    cir = QCircuit()
+    for q in q_state_1:
+        cir << H(q)
+    cir << X(q_state_2[0])
+    cir << QCmp.qubit_comparator(q_state_1, q_state_2, q_anc_cmp, function='g')
     prog << cir
-    res = m.prob_run_dict(prog, [q_anc_cmp[-1]])
-    print(res)
+    m.run(prog, 1000)
+    print(m.result().get_prob_dict([q_anc_cmp[-1]]))

pyqpanda-algorithm/example/QAlgBase/testeg_grover_markdata.py

@@ -1,24 +1,24 @@
-import pyqpanda as pq
-from pyqpanda_alg.QFinance import grover
+from pyqpanda3.core import CPUQVM, QProg
+
+from pyqpanda_alg.Grover import Grover, mark_data_reflection, iter_num, iter_analysis
 
 
 if __name__ == '__main__':
-    m = pq.CPUQVM()
-    m.initQVM()
-    q_state = m.qAlloc_many(3)
+    m = CPUQVM()
+    q_state = QProg(3).qubits()
 
     def mark(qubits):
-        return grover.mark_data_reflection(qubits=qubits, mark_data=['101', '001'])
-
+        return mark_data_reflection(qubits=qubits, mark_data=['101', '001'])
 
-    demo_search = grover.Grover(flip_operator=mark)
-    # iter_num = grover.iter_num(q_num=len(q_state), sol_num=2)
-    # print('best iter num: ', iter_num)
-    # prob, angle = grover.iter_analysis(q_num=len(q_state), sol_num=2, iternum=iter_num)
-    # print('prob for getting one of the solution with given iter num:', prob)
+    demo_search = Grover(flip_operator=mark)
+    best_iter = iter_num(q_num=len(q_state), sol_num=2)
+    print('best iter num: ', best_iter)
+    prob, angle = iter_analysis(q_num=len(q_state), sol_num=2, iternum=best_iter)
+    print('prob for getting one of the solution with given iter num:', prob)
 
-    prog = pq.QProg()
+    prog = QProg()
     prog << demo_search.cir(q_input=q_state)
 
-    res = m.prob_run_dict(prog, q_state)
-    print(res)
+    m.run(prog, 1000)
+    res = m.result().get_prob_dict(q_state)
+    print(res)

pyqpanda-algorithm/example/QAlgBase/testeg_t_spare.py

@@ -1,6 +1,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from pyqpanda_alg.QFinance.class_basic_sparecode import QSpare_Code
+from pyqpanda_alg.QSEncode import QSpare_Code
 def t01():
     mu = 0
     sigma = 1

pyqpanda-algorithm/pyqpanda_alg/Grover/Grover_core.py

@@ -81,7 +81,7 @@ def cir(self, q_input=None, q_flip=None, q_zero=None, iternum: int = 1):
         Examples
             An example for implementing an Grover search for state where q_0 `and` q_1 is 1.
 
-        >>> from pyqpanda3.core import CPUQVM, QCircuit, Z, TOFFOLI
+        >>> from pyqpanda3.core import CPUQVM, QCircuit, QProg, Z, TOFFOLI
         >>> from pyqpanda_alg import Grover
         >>> m = CPUQVM()
         >>> q_state = list(range(3))

pyqpanda-algorithm/pyqpanda_alg/Grover/__init__.py

@@ -1,5 +1,6 @@
 '''
-The QFinance module provides tools related to comparator, Quantum amplitude estimation, Grover algorithm, Grover optimization algorithm and QUBO problem solver, which are used to solve problems such as option pricing and portfolio optimization.
+The Grover module provides Grover search, its amplitude amplification operator and
+Grover adaptive search, used for unstructured search and combinatorial optimization.
 '''
 
 from .Grover_core import Grover,amp_operator,GroverAdaptiveSearch,mark_data_reflection,iter_num,iter_analysis

pyqpanda-algorithm/pyqpanda_alg/QAE/QAE.py

@@ -349,7 +349,6 @@ def run(self):
     def _measure(self, k: int, n_round: int) -> int:
         machine = self.machine
         qlist = self.qlist
-        clist = self.clist
 
         operator_g = amp_operator(in_operator=self.operatorA, q_input=qlist)
         prog = QProg()

pyqpanda-algorithm/pyqpanda_alg/QAOA/qaoa.py

@@ -482,7 +482,6 @@ def run_qaoa_circuit(self, gammas, betas, shots=-1):
 
         .. code-block:: python
 
-            import pyqpanda as pq
             import sympy as sp
             from pyqpanda_alg.QAOA.qaoa import *
 

pyqpanda-algorithm/pyqpanda_alg/QKmeans/QuantumKmeans.py

@@ -160,7 +160,6 @@ def fit(self, data):
 
 
         """
-        n = data.shape[0]
         c = data.shape[1]
 
         mean = np.mean(data, axis=0)

pyqpanda-algorithm/pyqpanda_alg/QSVD/QSVD.py

@@ -153,7 +153,6 @@ def return_diag(self, par):
         return abs(res)
 
     def max_eig(self, return_mat='0', par=None, max_index=0):
-        machine = CPUQVM()
         cir = QCircuit()
         ss = max_index % 2**self.q0
         bi0 = '{:b}'.format(ss).rjust(self.q0, '0')

pyqpanda-algorithm/pyqpanda_alg/QSVM/quantum_kernel_svm.py

@@ -209,7 +209,6 @@ def evaluate(self, x_vec: np.ndarray, y_vec: np.ndarray = None) -> np.ndarray:
 
                 import os
                 import numpy as np
-                import pyqpanda as pq
                 from sklearn.svm import SVC
                 import matplotlib
                 try:
@@ -350,8 +349,6 @@ def qsvm_classification():
             mus = np.asarray(mus.flat)
             nus = np.asarray(nus.flat)
 
-        is_statevector_sim = False
-        measurement = not is_statevector_sim
         measurement_basis = "0" * self._n_qbits
 
         for idx in range(0, len(mus), self._batch_size):

pyqpanda-algorithm/pyqpanda_alg/QUBO/QUBO.py

@@ -120,8 +120,6 @@ def query_qnumber(self) -> List[int]:
         """
         n_key = np.max([1, len(self.linear), len(self.quadratic)])
 
-        bounds = []
-
         def pos(x): return x > 0
         def neg(x): return x < 0
 

pyqpanda-algorithm/pyqpanda_alg/QmRMR/__init__.py

@@ -1,8 +1,9 @@
 '''
-The QFinance module provides tools related to comparator, Quantum amplitude estimation, Grover algorithm, Grover optimization algorithm and QUBO problem solver, which are used to solve problems such as option pricing and portfolio optimization.
+The QmRMR module provides quantum minimum-redundancy maximum-relevance feature
+selection, used to select an informative and non-redundant feature subset.
 '''
 
 
 from .QmRMR_core import Feature_Selection 
 
-__all__ = [Feature_Selection]
+__all__ = ['Feature_Selection']

pyqpanda-algorithm/pyqpanda_alg/plugin.py

@@ -185,10 +185,11 @@ def qft(qubit_list: list[int]) -> QCircuit:
         >>> # << H(0)
         >>> 
         >>> # 嵌入主程序执行
-        >>> qvm = pq.QMachine(pq.QMachineType.CPU)
-        >>> main_prog = pq.QProg()
+        >>> from pyqpanda3.core import CPUQVM, QProg
+        >>> qvm = CPUQVM()
+        >>> main_prog = QProg()
         >>> main_prog << qft_circuit  # 添加QFT电路
-        >>> qvm.run(main_prog)
+        >>> qvm.run(main_prog, 1000)
     """
     pi = 3.141592653589793238462643383279502884
 
@@ -260,9 +261,10 @@ def QFT(qubit_list: list[int]) -> QCircuit:
         >>> # 交换部分：SWAP(0,2)
         >>> 
         >>> # 嵌入主程序执行
-        >>> qvm = pq.QMachine(pq.QMachineType.CPU)
-        >>> main_prog = pq.QProg() << qft_circuit
-        >>> qvm.run(main_prog)
+        >>> from pyqpanda3.core import CPUQVM, QProg
+        >>> qvm = CPUQVM()
+        >>> main_prog = QProg() << qft_circuit
+        >>> qvm.run(main_prog, 1000)
     """
     pi = 3.141592653589793238462643383279502884
 
@@ -331,9 +333,10 @@ def bind_nonnegative_data(value: int, qubit_list: list[int]) -> QCircuit:
         >>> # 电路包含：X(0) << X(2)（对应二进制101，低位在前）
         >>> 
         >>> # 验证：量子比特0和2被翻转为|1⟩，1保持|0⟩
-        >>> qvm = pq.QMachine(pq.QMachineType.CPU)
-        >>> main_prog = pq.QProg() << circuit
-        >>> qvm.run(main_prog)
+        >>> from pyqpanda3.core import CPUQVM, QProg
+        >>> qvm = CPUQVM()
+        >>> main_prog = QProg() << circuit
+        >>> qvm.run(main_prog, 1000)
     """
     # 输入验证：value必须为非负整数
     if not isinstance(value, int):
@@ -407,16 +410,16 @@ def parse_quantum_result_dict(result: Dict[str, float], qubit_list: List[int], s
         >>> qubit_list = [0, 1, 2]
         >>> 
         >>> # 1. 返回所有结果
-        >>> parse_quantum_result(raw_result, qubit_list)
+        >>> parse_quantum_result_dict(raw_result, qubit_list)
         {'000': 0.1, '111': 0.8, '010': 0.1}
         >>> 
         >>> # 2. 返回概率最高的1个结果
-        >>> parse_quantum_result(raw_result, qubit_list, select_max=1)
+        >>> parse_quantum_result_dict(raw_result, qubit_list, select_max=1)
         {'111': 0.8}
         >>> 
         >>> # 3. 返回概率最高的2个结果
-        >>> parse_quantum_result(raw_result, qubit_list, select_max=2)
-        {'111': 0.8, '000': 0.1, '010': 0.1}  # 概率相同则保留原始顺序
+        >>> parse_quantum_result_dict(raw_result, qubit_list, select_max=2)
+        {'111': 0.8, '000': 0.1}  # 概率相同则保留原始顺序
     """
     # 输入类型验证
     if not isinstance(result, dict):