Matrix

Table of Contents

• Constructing a Matrix object
  • Default constructor
  • From numpy.ndarray
• Obtain internal data
  • Get numpy.ndarray
  • Get row total and col total
  • Get Element
• Application
  • Hermitian
  • Get transpose matrix
  • Get adjoint matrix
  • Get L2
• Boolean function
  • Equal

Prev Tutorial: Unitary
Next Tutorial: Quantum Information

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Abstraction of complex matrix

Constructing a Matrix object

Here is API doc

Default constructor

Python

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

Output

mat {
}

From numpy.ndarray

Construct based on the passed numpy.ndarray object storing a two-dimensional matrix

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()
 

Output

m {
{
{(0.633034,0)},{(0.196654,0)},{(0.0279015,0)},{(0.463236,0)},
}
{
{(0.625185,0)},{(0.263143,0)},{(0.785747,0)},{(0.788879,0)},
}
{
{(0.00441403,0)},{(0.96572,0)},{(0.803573,0)},{(0.889913,0)},
}
}

Obtain internal data

Get numpy.ndarray

Get internal data, the result is returned as a numpy.ndarray object

Here is API doc for Matrix.ndarray

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()
 

Output

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]]

Get row total and col total

Get the total number of rows and columns of the matrix

Here is API doc for Matrix.row_total

Here is API doc for Matrix.col_total

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()
 

Output

row total: 3
col total: 4

Get Element

Get the value of the matrix element by row index and column index

Here is API doc for Matrix.at

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()
 

Output

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),

Application

Hermitian

Determine whether the current matrix is Hermitian using is_hermitian()

Get transpose matrix

Get the transpose matrix of the current matrix using transpose() or T()

Get adjoint matrix

Get the adjoint matrix of the current matrix using hermitian_conjugate() or adjoint()

Get L2

Get the L2 norm of the current matrix using L2()

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()
 

Output

is_hermitian False
transpose {
{
{(0.111252,0)},{(0.61036,0)},{(0.4078,0)},
}
{
{(0.694994,0)},{(0.267013,0)},{(0.93402,0)},
}
{
{(0.172868,0)},{(0.167488,0)},{(0.635921,0)},
}
{
{(0,0)},{(0,0)},{(0,0)},
}
}
 
T {
{
{(0.111252,0)},{(0.61036,0)},{(0.4078,0)},
}
{
{(0.694994,0)},{(0.267013,0)},{(0.93402,0)},
}
{
{(0.172868,0)},{(0.167488,0)},{(0.635921,0)},
}
{
{(0,0)},{(0,0)},{(0,0)},
}
}
 
hermitian_conjugate {
{
{(0.111252,0)},{(0.61036,0)},{(0.4078,0)},
}
{
{(0.694994,0)},{(0.267013,0)},{(0.93402,0)},
}
{
{(0.172868,0)},{(0.167488,0)},{(0.635921,0)},
}
{
{(0.619626,0)},{(0.823553,0)},{(0.017278,0)},
}
}
 
adjoint {
{
{(0.111252,0)},{(0.61036,0)},{(0.4078,0)},
}
{
{(0.694994,0)},{(0.267013,0)},{(0.93402,0)},
}
{
{(0.172868,0)},{(0.167488,0)},{(0.635921,0)},
}
{
{(0.619626,0)},{(0.823553,0)},{(0.017278,0)},
}
}
 
L2 (1.6323515294877904+0j)

Boolean function

Equal

Determine whether the internal data of two Matrix objects are equal

Here is API doc

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()
 

Output

True