# qml.utils.decompose_hamiltonian¶

decompose_hamiltonian(H, hide_identity=False, wire_order=None)[source]

Decomposes a Hermitian matrix into a linear combination of Pauli operators.

Parameters
• H (array[complex]) – a Hermitian matrix of dimension $$2^n\times 2^n$$

• hide_identity (bool) – does not include the Identity observable within the tensor products of the decomposition if True

Returns

a list of coefficients and a list of corresponding tensor products of Pauli observables that decompose the Hamiltonian.

Return type

tuple[list[float], list[Observable]]

Example:

We can use this function to compute the Pauli operator decomposition of an arbitrary Hermitian matrix:

>>> A = np.array(
... [[-2, -2+1j, -2, -2], [-2-1j,  0,  0, -1], [-2,  0, -2, -1], [-2, -1, -1,  0]])
>>> coeffs, obs_list = decompose_hamiltonian(A)
>>> coeffs
[-1.0, -1.5, -0.5, -1.0, -1.5, -1.0, -0.5, 1.0, -0.5, -0.5]


We can use the output coefficients and tensor Pauli terms to construct a Hamiltonian:

>>> H = qml.Hamiltonian(coeffs, obs_list)
>>> print(H)
(-1.0) [I0 I1]
+ (-1.5) [X1]
+ (-0.5) [Y1]
+ (-1.0) [Z1]
+ (-1.5) [X0]
+ (-1.0) [X0 X1]
+ (-0.5) [X0 Z1]
+ (1.0) [Y0 Y1]
+ (-0.5) [Z0 X1]
+ (-0.5) [Z0 Y1]


This Hamiltonian can then be used in defining VQE problems using ExpvalCost.