H.sparse_matrix()

PennyLane Help
1 
from pennylane import FermiC, FermiA
from pennylane import jordan_wigner
from pennylane import numpy as np


h1 = 0.01 * (FermiC(0) * FermiA(0) + FermiC(1) * FermiA(1))
h2 = -0.02 * (FermiC(0) * FermiA(1) + FermiC(1) * FermiA(0))
h = h1 + h2
print(h)

h = jordan_wigner(h)
val, vec = np.linalg.eigh(h.sparse_matrix().toarray())
print(f"eigenvalues:\n{val}")
print()
print(f"eigenvectors:\n{np.real(vec.T)}")

error: ValueError: Output dtype not compatible with inputs.
2

Hey @jeet_sharma,

This code works fine for me and outputs the following:

0.01 * a⁺(0) a(0)
+ 0.01 * a⁺(1) a(1)
+ -0.02 * a⁺(0) a(1)
+ -0.02 * a⁺(1) a(0)
eigenvalues:
[-0.01  0.    0.02  0.03]

eigenvectors:
[[-0.         -0.70710678 -0.70710678 -0.        ]
 [ 1.          0.          0.          0.        ]
 [ 0.          0.          0.          1.        ]
 [ 0.         -0.70710678  0.70710678  0.        ]]

Here is my qml.about():

Name: PennyLane
Version: 0.35.0
Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Home-page: https://github.com/PennyLaneAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /Users/isaac/.virtualenvs/pennylane-stable/lib/python3.11/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane_Lightning

Platform info:           macOS-14.4-arm64-arm-64bit
Python version:          3.11.6
Numpy version:           1.26.4
Scipy version:           1.12.0
Installed devices:
- default.clifford (PennyLane-0.35.0)
- default.gaussian (PennyLane-0.35.0)
- default.mixed (PennyLane-0.35.0)
- default.qubit (PennyLane-0.35.0)
- default.qubit.autograd (PennyLane-0.35.0)
- default.qubit.jax (PennyLane-0.35.0)
- default.qubit.legacy (PennyLane-0.35.0)
- default.qubit.tf (PennyLane-0.35.0)
- default.qubit.torch (PennyLane-0.35.0)
- default.qutrit (PennyLane-0.35.0)
- null.qubit (PennyLane-0.35.0)
- lightning.qubit (PennyLane_Lightning-0.35.0)

Make sure you’re using the most up-to-date version of PennyLane! You can do so by doing pip install --upgrade pennylane. Hope this helps.