VisibleDeprecationWarning - 这是从哪里来的？
VisibleDeprecationWarning - where is this coming from?
我正在编写一些代码来模拟 python 中的量子计算机。我刚刚添加了一个开始集成大于一个量子位功能的部分，然后出现了这个奇怪的错误。它没有说明是哪条线引起的，所以我什至不知道从哪里开始修复它，而且我以前从未见过它。此外，该程序保持 运行ning 并在我 运行 的几个测试用例中输出正确答案，即使出现此错误也是如此。
错误
Warning (from warnings module):
File "/usr/lib/python3/dist-packages/numpy/lib/twodim_base.py", line 233
m = zeros((N, M), dtype=dtype)
VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
Warning (from warnings module):
File "/usr/lib/python3/dist-packages/numpy/lib/twodim_base.py", line 240
m[:M-k].flat[i::M+1] = 1
VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
完整节目
import cmath
import numpy as np
import math
from random import randint
def gate_scale(gate, ap_qubit):
dimensions = math.sqrt(np.size(gate))
ap_qubit-=1
if 2**qnum == dimensions:
return gate
else:
iterator = 1
kron_num = []
identity = np.identity(dimensions, np.matrix)
while iterator <= dimensions:
kron_num.append(identity)
iterator+=1
kron_num[ap_qubit] = gate
kron_iterator = list(range(len(kron_num)))
for i in kron_iterator:
if i == 0:
x = kron_num[i]
if i > 0:
x = np.kron(x, kron_num[i])
return x
def hadop(qstat, ap_qubit):
matrix = (1/cmath.sqrt(2))*np.array([[1,1],[1,-1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix, qstat)
def xop(qstat, ap_qubit):
matrix = np.array([[0,1],[1,0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def zop(qstat, ap_qubit):
matrix = np.array([[1,0],[0,-1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def yop(qstat, ap_qubit):
matrix = np.array([[0, cmath.sqrt(-1)],[-1*cmath.sqrt(-1),0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def sqrtxop(qstat, ap_qubit):
const1 = 1+cmath.sqrt(1)
const2 = 1-cmath.sqrt(1)
matrix = np.array([[const1/2,const2/2],[const2/2,const1/2]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def phaseshiftop(qstat, ap_qubit):
phasepos = [math.pi/4, math.pi/2]
print(phasepos)
x = input("Please pick one of the two phase shifts, 0 for the first, 1 for the second: ")
if x == "0":
y = phasepos[0]
elif x == "1":
y = phasepos[1]
const1 = cmath.sqrt(-1)*y
matrix = np.array([[1,0],[0,math.e**const1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
#use of eval because I want the user to be able to input constants, etc
def customop(qstat):
dimension = eval(input("What are the dimensions of your (square) matrix? Please input a single number: "))
ls = []
for y in range(dimension):
for x in range(dimension):
ls.append(float(input('What value for position ({}, {}): '.format(y+1, x+1))))
matrix = np.matrix(np.resize(ls, (dimension, dimension)))
#check if matrix is unitary
if np.array_equal(np.dot(matrix, matrix.conj().T), np.identity(dimension)) == True:
return np.dot(matrix, qstat)
else:
print("matrix not unitary, pretending none was applied")
return qstat
def probability(qstat, n): #fix to handle larger state vectors (see printing)
if n == 0:
return (qstat[0])**2
elif n == 1:
return (qstat[-1])**2
def measurement(qstat, ap_qubit): #fix to handle larger state vectors
prob1 = probability(qstat,0)
prob2 = probability(qstat,1)
random = randint(0,1)
if random <= prob1:
qstat = np.array([0,1])
elif prob1 < random:
qstat = np.array([1,0])
return qstat
qnum = int(input("how many qubits: "))
zero_state = np.matrix([[1],[0]])
one_state = np.matrix([[0],[1]])
z_or_o = input('would you like to start in the 0 or 1 state: ')
iterate = 1
while iterate <= qnum:
if iterate == 1:
if z_or_o == '0':
x = zero_state
elif z_or_o == '1':
x = one_state
if iterate == qnum:
qstat = x
print(qstat)
else:
x = np.kron(x,zero_state)
iterate+=1
gates = {"Hadamard":hadop, "X":xop, "Z":zop, "Y":yop, "sqrtX":sqrtxop,"phase shift":phaseshiftop,"measurement":measurement,"custom":customop}#, "control":control, "target":target
print(gates.keys())
done = "n"#needs to handle more than 1 qubit
while done == "n":
if qnum == 1:
fstgat = input("what gate would you like to use? use the list of gates at the top minus control and target: ")
ap_qubit = int(input("what qubit would you like it to be applied to?"))#handling control/target...
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input("Done with your circuit? y or n: ")
else:
print("sorry, that gate is not yet implemented. maybe try custom gate.")
else:
fstgat = input('what gate would you like to use? (proceed at your own risk): ')
ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input('done with your circuit? y or n: ')
else:
print('sorry, gate not implemented, maybe try custom gate.')
#printing - fix to handle larger state vectors
print(" ")
print("final state:", qstat)
print("probability of |0> state upon measurement is", probability(qstat,0))#this needs to iterate for qubits
print("probability of |1> state upon measurement is", probability(qstat,1))
（我把所有这些都包括进来是因为我不知道代码是从哪里来的。）
最小化代码
import cmath
import numpy as np
import math
from random import randint
def gate_scale(gate, ap_qubit):
dimensions = math.sqrt(np.size(gate))
ap_qubit-=1
if 2**qnum == dimensions:
return gate
else:
iterator = 1
kron_num = []
identity = np.identity(dimensions, np.matrix)
while iterator <= dimensions:
kron_num.append(identity)
iterator+=1
kron_num[ap_qubit] = gate
kron_iterator = list(range(len(kron_num)))
for i in kron_iterator:
if i == 0:
x = kron_num[i]
if i > 0:
x = np.kron(x, kron_num[i])
return x
def xop(qstat, ap_qubit):
matrix = np.array([[0,1],[1,0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def probability(qstat, n): #fix to handle larger state vectors (see printing)
if n == 0:
return (qstat[0])**2
elif n == 1:
return (qstat[-1])**2
def measurement(qstat, ap_qubit): #fix to handle larger state vectors
prob1 = probability(qstat,0)
prob2 = probability(qstat,1)
random = randint(0,1)
if random <= prob1:
qstat = np.array([0,1])
elif prob1 < random:
qstat = np.array([1,0])
return qstat
qnum = int(input("how many qubits: "))
zero_state = np.matrix([[1],[0]])
one_state = np.matrix([[0],[1]])
z_or_o = input('would you like to start in the 0 or 1 state: ')
iterate = 1
while iterate <= qnum:
if iterate == 1:
if z_or_o == '0':
x = zero_state
elif z_or_o == '1':
x = one_state
if iterate == qnum:
qstat = x
print(qstat)
else:
x = np.kron(x,zero_state)
iterate+=1
gates = {"Hadamard":hadop, "X":xop, "Z":zop, "Y":yop, "sqrtX":sqrtxop,"phase shift":phaseshiftop,"measurement":measurement,"custom":customop}#, "control":control, "target":target
print(gates.keys())
done = "n"#needs to handle more than 1 qubit
while done == "n":
if qnum == 1:
fstgat = input("what gate would you like to use? use the list of gates at the top minus control and target: ")
ap_qubit = int(input("what qubit would you like it to be applied to?"))#handling control/target...
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input("Done with your circuit? y or n: ")
else:
print("sorry, that gate is not yet implemented. maybe try custom gate.")
else:
fstgat = input('what gate would you like to use? (proceed at your own risk): ')
ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input('done with your circuit? y or n: ')
else:
print('sorry, gate not implemented, maybe try custom gate.')
#printing - fix to handle larger state vectors
print(" ")
print("final state:", qstat)
print("probability of |0> state upon measurement is", probability(qstat,0))#this needs to iterate for qubits
print("probability of |1> state upon measurement is", probability(qstat,1))
获取错误...
这是我遇到错误的 input/output。
how many qubits: 2
would you like to start in the 0 or 1 state: 0
[[1]
[0]
[0]
[0]]
dict_keys(['X', 'sqrtX', 'Hadamard', 'Z', 'phase shift', 'measurement', 'custom', 'Y'])
what gate would you like to use? (proceed at your own risk): X
what qubit would you like that gate to be applied to: 1
打印错误后，它将继续正常进行。
我很乐意添加任何其他必要的信息；让我知道。任何帮助将不胜感激。
It doesn't say anything about which line caused it, so I don't really even know where to start fixing it, and I've never seen it before.
一种简单的方法是将警告升级为异常，然后使用调试器检查变量。
所以你可以在前面加上这个：
import warnings
warnings.simplefilter("error", np.VisibleDeprecationWarning)
然后运行你的代码：
how many qubits: 2
would you like to start in the 0 or 1 state: 0
[[1]
[0]
[0]
[0]]
dict_keys(['X', 'Z', 'sqrtX', 'Hadamard', 'measurement', 'Y', 'custom', 'phase shift'])
what gate would you like to use? (proceed at your own risk): X
what qubit would you like that gate to be applied to: 1
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-e3d1cca2e826> in <module>()
136 ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
137 if fstgat in gates:
--> 138 qstat = gates[fstgat](qstat,ap_qubit)
139 done = input('done with your circuit? y or n: ')
140 else:
<ipython-input-43-e3d1cca2e826> in xop(qstat, ap_qubit)
36 def xop(qstat, ap_qubit):
37 matrix = np.array([[0,1],[1,0]])
---> 38 matrix = gate_scale(matrix, ap_qubit)
39 return np.dot(matrix,qstat)
40
<ipython-input-43-e3d1cca2e826> in gate_scale(gate, ap_qubit)
16 iterator = 1
17 kron_num = []
---> 18 identity = np.identity(dimensions, np.matrix)
19 while iterator <= dimensions:
20 kron_num.append(identity)
-\lib\site-packages\numpy\core\numeric.py in identity(n, dtype)
2392 """
2393 from numpy import eye
-> 2394 return eye(n, dtype=dtype)
2395
2396
\lib\site-packages\numpy\lib\twodim_base.py in eye(N, M, k, dtype)
178 if M is None:
179 M = N
--> 180 m = zeros((N, M), dtype=dtype)
181 if k >= M:
182 return m
TypeError: 'float' object cannot be interpreted as an integer
然后使用post-mortem analysis:
import pdb
pdb.pm()
> \lib\site-packages\numpy\lib\twodim_base.py(180)eye()
-> m = zeros((N, M), dtype=dtype)
(Pdb) args
N = 2.0
M = 2.0
k = 0
dtype = <class 'numpy.matrixlib.defmatrix.matrix'>
(Pdb) u
> \lib\site-packages\numpy\core\numeric.py(2394)identity()
-> return eye(n, dtype=dtype)
(Pdb) args
n = 2.0
dtype = <class 'numpy.matrixlib.defmatrix.matrix'>
如您所见，您传入了一个浮点数，但它需要一个整数。修复此警告后，您可以再次 运行 代码，看看是否还需要修复出现 VisibleDeprecationWarning 的其他地方。
我正在编写一些代码来模拟 python 中的量子计算机。我刚刚添加了一个开始集成大于一个量子位功能的部分，然后出现了这个奇怪的错误。它没有说明是哪条线引起的，所以我什至不知道从哪里开始修复它，而且我以前从未见过它。此外，该程序保持 运行ning 并在我 运行 的几个测试用例中输出正确答案，即使出现此错误也是如此。
错误
Warning (from warnings module):
File "/usr/lib/python3/dist-packages/numpy/lib/twodim_base.py", line 233
m = zeros((N, M), dtype=dtype)
VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
Warning (from warnings module):
File "/usr/lib/python3/dist-packages/numpy/lib/twodim_base.py", line 240
m[:M-k].flat[i::M+1] = 1
VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
完整节目
import cmath
import numpy as np
import math
from random import randint
def gate_scale(gate, ap_qubit):
dimensions = math.sqrt(np.size(gate))
ap_qubit-=1
if 2**qnum == dimensions:
return gate
else:
iterator = 1
kron_num = []
identity = np.identity(dimensions, np.matrix)
while iterator <= dimensions:
kron_num.append(identity)
iterator+=1
kron_num[ap_qubit] = gate
kron_iterator = list(range(len(kron_num)))
for i in kron_iterator:
if i == 0:
x = kron_num[i]
if i > 0:
x = np.kron(x, kron_num[i])
return x
def hadop(qstat, ap_qubit):
matrix = (1/cmath.sqrt(2))*np.array([[1,1],[1,-1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix, qstat)
def xop(qstat, ap_qubit):
matrix = np.array([[0,1],[1,0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def zop(qstat, ap_qubit):
matrix = np.array([[1,0],[0,-1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def yop(qstat, ap_qubit):
matrix = np.array([[0, cmath.sqrt(-1)],[-1*cmath.sqrt(-1),0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def sqrtxop(qstat, ap_qubit):
const1 = 1+cmath.sqrt(1)
const2 = 1-cmath.sqrt(1)
matrix = np.array([[const1/2,const2/2],[const2/2,const1/2]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def phaseshiftop(qstat, ap_qubit):
phasepos = [math.pi/4, math.pi/2]
print(phasepos)
x = input("Please pick one of the two phase shifts, 0 for the first, 1 for the second: ")
if x == "0":
y = phasepos[0]
elif x == "1":
y = phasepos[1]
const1 = cmath.sqrt(-1)*y
matrix = np.array([[1,0],[0,math.e**const1]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
#use of eval because I want the user to be able to input constants, etc
def customop(qstat):
dimension = eval(input("What are the dimensions of your (square) matrix? Please input a single number: "))
ls = []
for y in range(dimension):
for x in range(dimension):
ls.append(float(input('What value for position ({}, {}): '.format(y+1, x+1))))
matrix = np.matrix(np.resize(ls, (dimension, dimension)))
#check if matrix is unitary
if np.array_equal(np.dot(matrix, matrix.conj().T), np.identity(dimension)) == True:
return np.dot(matrix, qstat)
else:
print("matrix not unitary, pretending none was applied")
return qstat
def probability(qstat, n): #fix to handle larger state vectors (see printing)
if n == 0:
return (qstat[0])**2
elif n == 1:
return (qstat[-1])**2
def measurement(qstat, ap_qubit): #fix to handle larger state vectors
prob1 = probability(qstat,0)
prob2 = probability(qstat,1)
random = randint(0,1)
if random <= prob1:
qstat = np.array([0,1])
elif prob1 < random:
qstat = np.array([1,0])
return qstat
qnum = int(input("how many qubits: "))
zero_state = np.matrix([[1],[0]])
one_state = np.matrix([[0],[1]])
z_or_o = input('would you like to start in the 0 or 1 state: ')
iterate = 1
while iterate <= qnum:
if iterate == 1:
if z_or_o == '0':
x = zero_state
elif z_or_o == '1':
x = one_state
if iterate == qnum:
qstat = x
print(qstat)
else:
x = np.kron(x,zero_state)
iterate+=1
gates = {"Hadamard":hadop, "X":xop, "Z":zop, "Y":yop, "sqrtX":sqrtxop,"phase shift":phaseshiftop,"measurement":measurement,"custom":customop}#, "control":control, "target":target
print(gates.keys())
done = "n"#needs to handle more than 1 qubit
while done == "n":
if qnum == 1:
fstgat = input("what gate would you like to use? use the list of gates at the top minus control and target: ")
ap_qubit = int(input("what qubit would you like it to be applied to?"))#handling control/target...
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input("Done with your circuit? y or n: ")
else:
print("sorry, that gate is not yet implemented. maybe try custom gate.")
else:
fstgat = input('what gate would you like to use? (proceed at your own risk): ')
ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input('done with your circuit? y or n: ')
else:
print('sorry, gate not implemented, maybe try custom gate.')
#printing - fix to handle larger state vectors
print(" ")
print("final state:", qstat)
print("probability of |0> state upon measurement is", probability(qstat,0))#this needs to iterate for qubits
print("probability of |1> state upon measurement is", probability(qstat,1))
（我把所有这些都包括进来是因为我不知道代码是从哪里来的。）
最小化代码
import cmath
import numpy as np
import math
from random import randint
def gate_scale(gate, ap_qubit):
dimensions = math.sqrt(np.size(gate))
ap_qubit-=1
if 2**qnum == dimensions:
return gate
else:
iterator = 1
kron_num = []
identity = np.identity(dimensions, np.matrix)
while iterator <= dimensions:
kron_num.append(identity)
iterator+=1
kron_num[ap_qubit] = gate
kron_iterator = list(range(len(kron_num)))
for i in kron_iterator:
if i == 0:
x = kron_num[i]
if i > 0:
x = np.kron(x, kron_num[i])
return x
def xop(qstat, ap_qubit):
matrix = np.array([[0,1],[1,0]])
matrix = gate_scale(matrix, ap_qubit)
return np.dot(matrix,qstat)
def probability(qstat, n): #fix to handle larger state vectors (see printing)
if n == 0:
return (qstat[0])**2
elif n == 1:
return (qstat[-1])**2
def measurement(qstat, ap_qubit): #fix to handle larger state vectors
prob1 = probability(qstat,0)
prob2 = probability(qstat,1)
random = randint(0,1)
if random <= prob1:
qstat = np.array([0,1])
elif prob1 < random:
qstat = np.array([1,0])
return qstat
qnum = int(input("how many qubits: "))
zero_state = np.matrix([[1],[0]])
one_state = np.matrix([[0],[1]])
z_or_o = input('would you like to start in the 0 or 1 state: ')
iterate = 1
while iterate <= qnum:
if iterate == 1:
if z_or_o == '0':
x = zero_state
elif z_or_o == '1':
x = one_state
if iterate == qnum:
qstat = x
print(qstat)
else:
x = np.kron(x,zero_state)
iterate+=1
gates = {"Hadamard":hadop, "X":xop, "Z":zop, "Y":yop, "sqrtX":sqrtxop,"phase shift":phaseshiftop,"measurement":measurement,"custom":customop}#, "control":control, "target":target
print(gates.keys())
done = "n"#needs to handle more than 1 qubit
while done == "n":
if qnum == 1:
fstgat = input("what gate would you like to use? use the list of gates at the top minus control and target: ")
ap_qubit = int(input("what qubit would you like it to be applied to?"))#handling control/target...
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input("Done with your circuit? y or n: ")
else:
print("sorry, that gate is not yet implemented. maybe try custom gate.")
else:
fstgat = input('what gate would you like to use? (proceed at your own risk): ')
ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
if fstgat in gates:
qstat = gates[fstgat](qstat,ap_qubit)
done = input('done with your circuit? y or n: ')
else:
print('sorry, gate not implemented, maybe try custom gate.')
#printing - fix to handle larger state vectors
print(" ")
print("final state:", qstat)
print("probability of |0> state upon measurement is", probability(qstat,0))#this needs to iterate for qubits
print("probability of |1> state upon measurement is", probability(qstat,1))
获取错误...
这是我遇到错误的 input/output。
how many qubits: 2
would you like to start in the 0 or 1 state: 0
[[1]
[0]
[0]
[0]]
dict_keys(['X', 'sqrtX', 'Hadamard', 'Z', 'phase shift', 'measurement', 'custom', 'Y'])
what gate would you like to use? (proceed at your own risk): X
what qubit would you like that gate to be applied to: 1
打印错误后，它将继续正常进行。
我很乐意添加任何其他必要的信息；让我知道。任何帮助将不胜感激。
It doesn't say anything about which line caused it, so I don't really even know where to start fixing it, and I've never seen it before.
一种简单的方法是将警告升级为异常，然后使用调试器检查变量。
所以你可以在前面加上这个：
import warnings
warnings.simplefilter("error", np.VisibleDeprecationWarning)
然后运行你的代码：
how many qubits: 2
would you like to start in the 0 or 1 state: 0
[[1]
[0]
[0]
[0]]
dict_keys(['X', 'Z', 'sqrtX', 'Hadamard', 'measurement', 'Y', 'custom', 'phase shift'])
what gate would you like to use? (proceed at your own risk): X
what qubit would you like that gate to be applied to: 1
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-e3d1cca2e826> in <module>()
136 ap_qubit = int(input('what qubit would you like that gate to be applied to: '))
137 if fstgat in gates:
--> 138 qstat = gates[fstgat](qstat,ap_qubit)
139 done = input('done with your circuit? y or n: ')
140 else:
<ipython-input-43-e3d1cca2e826> in xop(qstat, ap_qubit)
36 def xop(qstat, ap_qubit):
37 matrix = np.array([[0,1],[1,0]])
---> 38 matrix = gate_scale(matrix, ap_qubit)
39 return np.dot(matrix,qstat)
40
<ipython-input-43-e3d1cca2e826> in gate_scale(gate, ap_qubit)
16 iterator = 1
17 kron_num = []
---> 18 identity = np.identity(dimensions, np.matrix)
19 while iterator <= dimensions:
20 kron_num.append(identity)
-\lib\site-packages\numpy\core\numeric.py in identity(n, dtype)
2392 """
2393 from numpy import eye
-> 2394 return eye(n, dtype=dtype)
2395
2396
\lib\site-packages\numpy\lib\twodim_base.py in eye(N, M, k, dtype)
178 if M is None:
179 M = N
--> 180 m = zeros((N, M), dtype=dtype)
181 if k >= M:
182 return m
TypeError: 'float' object cannot be interpreted as an integer
然后使用post-mortem analysis:
import pdb
pdb.pm()
> \lib\site-packages\numpy\lib\twodim_base.py(180)eye()
-> m = zeros((N, M), dtype=dtype)
(Pdb) args
N = 2.0
M = 2.0
k = 0
dtype = <class 'numpy.matrixlib.defmatrix.matrix'>
(Pdb) u
> \lib\site-packages\numpy\core\numeric.py(2394)identity()
-> return eye(n, dtype=dtype)
(Pdb) args
n = 2.0
dtype = <class 'numpy.matrixlib.defmatrix.matrix'>
如您所见，您传入了一个浮点数，但它需要一个整数。修复此警告后，您可以再次 运行 代码，看看是否还需要修复出现 VisibleDeprecationWarning 的其他地方。