Characterization of variational quantum algorithms using free fermions

We study variational quantum algorithms from the perspective of free

fermions. By deriving the explicit structure of the associated Lie algebras, we

show that the Quantum Approximate Optimization Algorithm (QAOA) on a

one-dimensional lattice -- with and without decoupled angles -- is able to

prepare all fermionic Gaussian states respecting the symmetries of the circuit.

Leveraging these results, we numerically study the interplay between these

symmetries and the locality of the target state, and find that an absence of

symmetries makes nonlocal states easier to prepare. An efficient classical

simulation of Gaussian states, with system sizes up to $80$ and deep circuits,

is employed to study the behavior of the circuit when it is overparameterized.

In this regime of optimization, we find that the number of iterations to

converge to the solution scales linearly with system size. Moreover, we observe

that the number of iterations to converge to the solution decreases

exponentially with the depth of the circuit, until it saturates at a depth

which is quadratic in system size. Finally, we conclude that the improvement in

the optimization can be explained in terms of of better local linear

approximations provided by the gradients.