 # Using expm vs euler formula in default.qubit rotations

#1

Python: Python 3.7
PennyLane: 0.4
Scipy: 1.3.0
Numpy: 1.16.4

Hi there,

I am currently experimenting quantum neural networks in PennyLane + Tensorflow. When profiling my code I found that subsequent calls to RotX, RotY and RotZ in pennylane.plugins.default_qubit were taking up a large part of the gradient computation.

In PennyLane these rotations are implemented with the the Scipy expm() function. However, I think there is a more efficient way to compute these exponentials. By making use of Euler’s Identity (I have the proof somewhere if required) we can rewrite the matrix exponentiation as

\begin{align} \exp(a \vec{n}\cdot \vec{\sigma}) &=I\cosh{(a)}+ \sinh{(a)}(\vec{n}\cdot \vec{\sigma}) \end{align}

which implies

\begin{align} \exp(a \sigma_i) &=I\cosh{(a)}+ \sinh{(a)}\sigma_i\\ \exp(ia \sigma_i) &=I\cos{(a)}+ i\sin{(a)}\sigma_i \end{align}

This form of the matrix exponent of is computationally much cheaper than the one from Scipy, which implements a Pade approximation. This is demonstrated by the following code:

from scipy.linalg import expm
import numpy as np
import time
import matplotlib.pyplot as plt

I = np.array([[1,0], [0,1]]) #: Pauli-X matrix
X = np.array([[0, 1], [1, 0]]) #: Pauli-X matrix
Y = np.array([[0, -1j], [1j, 0]]) #: Pauli-Y matrix
Z = np.array([[1, 0], [0, -1]]) #: Pauli-Z matrix

# pennylane.plugins.default_qubit implementation
def Rot_pennylane(theta):
return expm(-1j * theta/2 * X)
# implementation with Euler's formula
def Rot_euler(theta):
return np.cos(theta/2) * I + 1j * np.sin(-theta/2) * X

# assert wheter or not these methods return the same matrices for all angles of theta
close = []
for x in np.linspace(0,2 * np.pi, 100):
close.append(np.all(np.isclose(Rot_pennylane(x), Rot_euler(x))))
print("Are the methods equivalent? {}".format(all(close)))

deltat_pl = []
deltat_euler = []
num_its = int(10 ** 3)
# gather statistics for average runtime for PennyLane implemenation
for i in range(num_its):
start = time.time()
_ = Rot_pennylane(4 * np.pi * np.random.rand() - 2 * np.pi)
end = time.time()
deltat_pl.append(end - start)

# gather statistics for average runtime for Euler implemenation
for i in range(num_its):
start = time.time()
_ = Rot_euler(4 * np.pi * np.random.rand() - 2 * np.pi)
end = time.time()
deltat_euler.append(end - start)

# print statistics
print("Pennylane average: t = {:.2E} sec".format(np.mean(deltat_pl)))
print("Euler average: t = {:.2E} sec".format(np.mean(deltat_euler)))
print("Speedup factor: n = {:1.2f}x".format(np.mean(deltat_pl) / np.mean(deltat_euler)))

# plot runtime distributions
fig, ax = plt.subplots(1,1)
ax.hist(deltat_pl, label='PennyLane = $\mu=${:.2E}'.format(np.mean(deltat_pl)))
ax.hist(deltat_euler, label='Euler = $\mu=${:.2E}'.format(np.mean(deltat_euler)))
plt.legend()
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlabel('$\log_{10}$(time) (s)')
ax.set_ylabel('$\log_{10}$(N)')
ax.set_title('Performance comparison of matrix exponentiation')
plt.show()



Which shows a >30x speedup.

Are the methods equivalent? True
Pennylane average: t = 2.62E-04 sec
Euler average: t = 8.58E-06 sec
Speedup factor: n = 30.57x


Do you agree with my observations?

Regards,
Roeland

#2

Hi @rooler! That is a great find (and great analysis/benchmarking), thank you for letting us know.

This is definitely something we would like to improve in default.qubit, and we are always happy to accept contributions if you would like to make a pull request to the GitHub repository.

Otherwise, this is a change we will likely make in the GitHub master branch, ready for the next release (0.5) of PennyLane.

#3

If you come across any other performance improvements in default.qubit, please let us know, either by making a post here, creating a GitHub issue, or by submitting a pull request with the fix directly.

#4

Great, I’ll make a pull request for this.

I had some other issues (some of which I solved) when working with the TensorFlow interface that I will post here as well.