# How do I optimize HHL algorithm in Qiskit?

How do I optimize HHL algorithm in Qiskit?

I tried to follow this tutorial on HHL in Qiskit. My project requires solving a very specific type of linear equations $$Ax=b$$ like the one below.

b = np.array( [ 0.   ,  0.   ,  2.25 ,  0.   ,  0.   ,  0.   , -4.285,  0.   ])
A = np.array([[ 1.   ,  0.333,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],
[ 0.   ,  1.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],
[ 0.   ,  0.   ,  1.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],
[ 0.   ,  0.   ,  0.143,  1.   ,  0.   ,  0.   ,  0.   ,  0.   ],
[-0.333, -1.   ,  0.   ,  0.   ,  1.   ,  0.333,  0.   ,  0.   ],
[-0.25 , -1.5  , -0.25 ,  0.   ,  0.   ,  1.   ,  0.   ,  0.   ],
[ 0.   , -0.562, -0.875, -0.562,  0.   ,  0.   ,  1.   ,  0.   ],
[ 0.   ,  0.   , -1.   , -0.143,  0.   ,  0.   ,  0.143,  1.   ]])

However, I only obtain a fidelity of around 0.7 - 0.8, which is not good enough for what I would like to do.

I notice the tutorial has a function called create_eigs with input parameters num_ancillae, num_time_slices, negative_evals.

def create_eigs(matrix, num_ancillae, num_time_slices, negative_evals):
...

What are they really? And what are their appropriate values?