  The quantum approximate optimization algorithm (QAOA) promises to solve
classically intractable computational problems in the area of combinatorial
optimization. A growing amount of evidence suggests that the originally
proposed form of the QAOA ansatz is not optimal, however. To address this
problem, we propose an alternative ansatz, which we call QAOA+, that augments
the traditional $p = 1$ QAOA ansatz with an additional multiparameter
problem-independent layer. The QAOA+ ansatz allows obtaining higher
approximation ratios than $p = 1$ QAOA while keeping the circuit depth below
that of $p = 2$ QAOA, as benchmarked on the MaxCut problem for random regular
graphs. We additionally show that the proposed QAOA+ ansatz, while using a
larger number of trainable classical parameters than with the standard QAOA, in
most cases outperforms alternative multiangle QAOA ans\"atze.
