矩阵

目录

• 构造 Matrix 对象
  • 默认构造函数
  • numpy.ndarray 作为构造方法入参
• 获取内部数据
  • 获取 numpy.ndarray 形式的数据
  • 获取行总数和列总数
  • 获取元素
• 应用
  • 厄米性
  • 获取转置矩阵
  • 获取伴随矩阵
  • 获取 L2 范数
• 布尔函数
  • 判等

上一章: 酉矩阵
下一章: 量子信息

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复杂矩阵的抽象。

构造 Matrix 对象

API文档链接

默认构造函数

Python

 
from pyqpanda3.quantum_info import Matrix
# Constructing a Matrix object
mat = Matrix()
# Print result
print("mat",mat)
 

执行结果如下所示：

mat []

numpy.ndarray 作为构造方法入参

根据传入的 numpy.ndarray 对象构造，该对象存储了一个二维矩阵。

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
    # Print result
    print("m",m)
 
if __name__ == "__main__":
    test()
 

执行结果如下所示：

m [[0.6650874 +0.j 0.68517404+0.j 0.98889109+0.j 0.04471197+0.j]
 [0.64401864+0.j 0.39456866+0.j 0.96256409+0.j 0.53353689+0.j]
 [0.72914706+0.j 0.01258831+0.j 0.46879808+0.j 0.41446852+0.j]]

获取内部数据

获取 numpy.ndarray 形式的数据

获取内部数据，结果将作为 numpy.ndarray 对象返回。

API文档链接

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
    data_ = m.ndarray()
    # Print result
    print("data_",data)
 
if __name__ == "__main__":
    test()
 

执行结果如下所示：

data_ [[0.03511921+0.j 0.75430961+0.j 0.19238195+0.j 0.19057673+0.j]
 [0.61574108+0.j 0.89369063+0.j 0.81701418+0.j 0.73896602+0.j]
 [0.21010302+0.j 0.35964433+0.j 0.41305091+0.j 0.44447268+0.j]]

获取行总数和列总数

获取矩阵的行总数和列总数。

获取总行数API文档链接

获取总列数API文档链接

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
    # Print row total
    print("row total:",m.row_total())
    # Print col total
    print("col total:",m.col_total())
if __name__ == "__main__":
    test()
 

执行结果如下所示：

row total: 3
col total: 4

获取元素

通过行索引和列索引获取矩阵元素的值。

API文档链接

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
 
    # Get the value of the matrix element by row index and column index
    print("m:")
    for i in range(m.row_total()):
        for j in range(m.col_total()):
            print(m.at(i,j),end=',') # at function for Get the value of the matrix element by row index and column index
        print()
if __name__ == "__main__":
    test()
 

执行结果如下所示：

m:
(0.5387399873890723+0j),(0.5946435956404229+0j),(0.8662388940809534+0j),(0.7465917268170127+0j),
(0.6650096219471038+0j),(0.10914316657637124+0j),(0.5999827262878416+0j),(0.5121762859881978+0j),
(0.43882836608920894+0j),(0.1639091992188828+0j),(0.4683573326093392+0j),(0.9735070812593357+0j),

应用

厄米性

判断当前矩阵是否为 Hermitian 矩阵，使用 is_hermitian 接口。

获取转置矩阵

获取当前矩阵的转置矩阵，使用 transpose 或 T 接口。

获取伴随矩阵

获取当前矩阵的伴随矩阵，使用 hermitian_conjugate 或 adjoint 接口。

获取 L2 范数

获取当前矩阵的 L2 范数，使用 L2 接口。

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
 
    print("is_hermitian",m.is_hermitian())# Determine whether the current matrix is Hermitian
    print("transpose",m.transpose())# Get the transpose matrix of the current matrix
    print("T",m.T())# Get the transpose matrix of the current matrix
    print("hermitian_conjugate",m.hermitian_conjugate()) # Get the adjoint matrix of the current matrix
    print("adjoint",m.adjoint()) # Get the adjoint matrix of the current matrix
    print("L2",m.L2()) # Get the L2 norm of the current matrix
if __name__ == "__main__":
    test()
 

执行结果如下所示：

is_hermitian False
transpose [[0.9341786 +0.j 0.2134688 +0.j 0.40507989+0.j]
 [0.49122518+0.j 0.45268175+0.j 0.10207822+0.j]
 [0.062246  +0.j 0.65736666+0.j 0.15927297+0.j]
 [0.        +0.j 0.        +0.j 0.        +0.j]]
T [[0.9341786 +0.j 0.2134688 +0.j 0.40507989+0.j]
 [0.49122518+0.j 0.45268175+0.j 0.10207822+0.j]
 [0.062246  +0.j 0.65736666+0.j 0.15927297+0.j]
 [0.        +0.j 0.        +0.j 0.        +0.j]]
hermitian_conjugate [[0.9341786 +0.j 0.2134688 +0.j 0.40507989+0.j]
 [0.49122518+0.j 0.45268175+0.j 0.10207822+0.j]
 [0.062246  +0.j 0.65736666+0.j 0.15927297+0.j]
 [0.46341251+0.j 0.80009271+0.j 0.31558019+0.j]]
adjoint [[0.9341786 +0.j 0.2134688 +0.j 0.40507989+0.j]
 [0.49122518+0.j 0.45268175+0.j 0.10207822+0.j]
 [0.062246  +0.j 0.65736666+0.j 0.15927297+0.j]
 [0.46341251+0.j 0.80009271+0.j 0.31558019+0.j]]
L2 (1.5595857384665406+0j)

布尔函数

判等

判断两个 Matrix 对象的内部数据是否相等。

API文档链接

Python

 
from pyqpanda3.quantum_info import Matrix
import numpy as np
def test():
    # prepare  two-dimensional matrix
    rows = 3
    cols = 4
    data = np.random.rand(rows, cols)
    data = np.array(data, dtype=complex)
    # Constructing a Matrix object
    m = Matrix(data)
    # Constructing another Matrix object
    m2 = m
    # Determine whether the internal data of two Matrix objects are equal
    print(m==m2)
if __name__ == "__main__":
    test()
 

执行结果如下所示：

True