Add scalar multiplication `__mul__` method to `SparseHamiltonian`

Suppose we create a Sparse Hamiltonian

import pennylane as qml
from scipy import sparse

sparse_mat = sparse.coo_matrix(np.array([[1.0, 2.0], [2.0, 3.0]]))

h = qml.SparseHamiltonian(sparse_mat, wires=[0])

If we multiply this by a float, we get back a Hamiltonian object pairing the SparseHamiltonian with a coefficient:

>>> new_h = -1.0 * h
>>> print(new_h)
(-1.0) [SparseHamiltonian0]

We should instead create a new SparseHamiltonian object with a new matrix. This way we can keep the benefits of having a SparseHamiltonian object.

This would be accomplished by defining the __mul__ and __rmul__ dunder methods in SparseHamiltonian located in pennylane/ops/qubit/observables.py

[antalszava]

Hi @tgag17, sure 🙂 Feel free to reach out for help/when ready for the PR to be reviewed.

[antalszava]

Hi @tgag17, apologies for the late reply. Changes in the master branch have made the original snippet outdated and also seem to have made this issue be resolved (cc @albi3ro):

import pennylane as qml
from scipy import sparse
import numpy as np

sparse_mat = sparse.csr_matrix(np.array([[1.0, 2.0], [2.0, 3.0]]))

h = qml.SparseHamiltonian(sparse_mat, wires=[0])

new_h = -1.0 * h
print(new_h)

(-1.0) [SparseHamiltonian0]