Using Qiskit VQE for solving an eigenvalue problem

Question

I am a new user of Qiskit and I believe there is a simple answer to my question but I have had a very hard time finding a straightforward answer. I am trying to transform a given $3 \times 3$ (Hermitian) Hamiltonian matrix into a qubit operator and then use the built-in VQE solver for evaluating the minimum eigenvalue for said matrix.

I have been successful in doing something similar using the qiskit.chemistry.FermionicOperator built-in class (see example below) but for this example the Hamiltonian is mapped to a Fermionic one-body qubit Hamiltonian which has $2^n$ eigenvalues instead of $n$ eigenvalues (where $n=3$ for this case). I am hoping there is a simple way to directly map a matrix to a qubit operator. My vague understanding is I need to write the matrix as a sum of weighted Pauli operators but I would expect there would exist some predefined function for doing this? (I have looked at the source code for the FermionicOperator class but it did not help me much).

import numpy as np
import scipy
import h5py

from qiskit.aqua.algorithms import VQE, NumPyEigensolver
from qiskit.chemistry import FermionicOperator
from qiskit import Aer

backend = Aer.get_backend("qasm_simulator")

c1=1
c2=2
c3=3

ff=np.zeros((3,3))
ff[1,1]=2
ff[2,2]=1

n=3

Hamil=np.zeros((n,n))
Hamil[1,1]=c2
Hamil[2,2]=-c3/2+c2
Hamil[0,2]=np.sqrt(2)*c1
Hamil[1,2]=np.sqrt(2)*c1
Hamil[2,0]=np.sqrt(2)*c1
Hamil[2,1]=np.sqrt(2)*c1

vals,vecs=np.linalg.eig(Hamil)

print("Standard Eigenvalues: ")
print(vals)

Hamil_op = FermionicOperator(h1=Hamil)
Hamil_ops = Hamil_op.mapping(map_type='parity', threshold=1e-12)
result = NumPyEigensolver(Hamil_ops,k=int(2**n)).run()

print("Quibit Eigenvalues=")
print(result['eigenvalues'])

vqe = VQE(operator=Hamil_ops)
vqe_result = np.real(vqe.run(backend)['eigenvalue'])
print("VQE Eigenvalue")
print(vqe_result)

With result:

Standard Eigenvalues:
[-1.45426242  0.90078898  3.05347344]
Quibit Eigenvalues=
[-1.45426242+0.j -0.55347344+0.j  0.        +0.j  0.90078898+0.j
  1.59921102+0.j  2.5       +0.j  3.05347344+0.j  3.95426242+0.j]
VQE Eigenvalue
0.18608074335373637

Answer 1

I believe to have answered my own question through further attempts. Per the comment from KAJ226, it is true that a $(3 \times 3)$ matrix cannot be represented as a string of Paulis so I have nested the $(3 \times 3)$ matrix into a $(4 \times 4)$ matrix where the final row and column are all zeros. From this I can omit the useless eigenvalue/eigenvector and just retain the relevant results for the $(3 \times 3)$ matrix.

My solution mostly came from using the WeightedPauliOperator, MatrixOperator, and op_converter classes. Below is a sample of my code with corresponding outputs. Thanks to those who gave suggestions to help me out!

import numpy as np
import scipy
import h5py

from qiskit.aqua.algorithms import VQE, NumPyEigensolver
from qiskit.aqua.operators import WeightedPauliOperator, MatrixOperator, op_converter
from qiskit import Aer

backend = Aer.get_backend("qasm_simulator")

c1=-0.5
c2=.75
c3=2

n1=3
Hamil=np.zeros((n1,n1))
Hamil[1,1]=c2
Hamil[2,2]=-c3/2+c2
Hamil[0,2]=c1
Hamil[1,2]=c1
Hamil[2,0]=c1
Hamil[2,1]=c1

print("(3x3) Hamiltonian")
print(Hamil)

vals,vecs=np.linalg.eig(Hamil)

print("Standard Eigenvalues: ")
print(vals)

n2=4
Hamil=np.zeros((n2,n2))
Hamil[1,1]=c2
Hamil[2,2]=-c3/2+c2
Hamil[0,2]=c1
Hamil[1,2]=c1
Hamil[2,0]=c1
Hamil[2,1]=c1

print("(4x4) Hamiltonian")
print(Hamil)

Hamil_Mat=MatrixOperator(Hamil)
Hamil_Qop = op_converter.to_weighted_pauli_operator(Hamil_Mat)

q_vals = NumPyEigensolver(Hamil_Qop,k=4).run()

print("Qubit Op Eigenvalues: ")
print(q_vals['eigenvalues'])

vqe=VQE(Hamil_Qop)
vqe_result=vqe.run(backend)

print("VQE Eigenvalue: ")
print(vqe_result['eigenvalue'])

With result:

(3x3) Hamiltonian
[[ 0.    0.   -0.5 ]
 [ 0.    0.75 -0.5 ]
 [-0.5  -0.5  -0.25]]
Standard Eigenvalues:
[-0.75  0.25  1.  ]
(4x4) Hamiltonian
[[ 0.    0.   -0.5   0.  ]
 [ 0.    0.75 -0.5   0.  ]
 [-0.5  -0.5  -0.25  0.  ]
 [ 0.    0.    0.    0.  ]]
Qubit Op Eigenvalues:
[-0.75+0.j  0.  +0.j  0.25+0.j  1.  +0.j]
VQE Eigenvalue:
(-0.705078125+0j)