  The human brain has inspired novel concepts complementary to classical and
quantum computing architectures, such as artificial neural networks and
neuromorphic computers, but it is not clear how their performances compare.
Here we report a new methodological framework for benchmarking cognitive
performance based on solving computational problems with increasing problem
size. We determine computational efficiencies in experiments with human
participants and benchmark these against complexity classes. We show that a
neuromorphic architecture with limited field-of-view size and added noise
provides a good approximation to our results. The benchmarking also suggests
there is no quantum advantage on the scales of human capability compared to the
neuromorphic model. Thus, the framework offers unique insights into the
computational efficiency of the brain by considering it a black box.
