问题
I'm writing some code to simulate a quantum computer in python. I just added a section which starts integrating greater-than-one-qubit functionality, and then this weird error came up. 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. Further, the program keeps running and outputs the correct answer in the few testcases I've run, even with this error.
Error
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
Full Program
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))
(I'm including all this because I don't know where the code is coming from.)
Minimized code
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))
To get the error...
This is the input/output that got the error for me.
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
After the error gets printed it continues to carry on normally.
I'd be glad to add any other information necessary; just let me know. Any help would be appreciated.
回答1:
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.
An easy way would be to promote warnings to exceptions and then use a debugger to inspect the variables.
So you could prepend this:
import warnings
warnings.simplefilter("error", np.VisibleDeprecationWarning)
Then run your code:
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
Then use 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'>
As you can see you passed in a float but it expected an integer. When you fixed this warning you can run the code again and see if you also need to fix other places where the VisibleDeprecationWarnings came up.
来源：https://stackoverflow.com/questions/46269261/visibledeprecationwarning-where-is-this-coming-from