Hi everyone. I’m working on quantum neural networks for computer vision. I wonder if there is a better or more efficient way to implement quantum version of convolution rather than using a number of for loops like the one demonstrated in this tutorial? Josh suggested me to use Dask to parallelize QNode computations (Thanks @josh again! ). However, it seems I did not use it correctly because when I executed the demo codes provided in the documentation of Dask, I found the computation got even much slower. Here is the code I run:

```
import dask
import numpy as np
def inc(x):
return x + 1
def double(x):
return x * 2
def add(x, y):
return x + y
data = np.random.rand((100))
t0 = time.time()
output = []
for x in data:
a = dask.delayed(inc)(x)
b = dask.delayed(double)(x)
c = dask.delayed(add)(a, b)
output.append(c)
total = dask.delayed(sum)(output)
total.compute()
t1 = time.time()
print("Time: ", (t1 - t0) )
t0 = time.time()
output = []
for x in data:
a = (inc)(x)
b = (double)(x)
c = (add)(a, b)
output.append(c)
total = (sum)(output)
t1 = time.time()
print("Time: ", (t1 - t0) )
Time: 0.034467220306396484
Time: 0.0003325939178466797
```

Could anyone help me with it? Many thanks!