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.

翻译：人类大脑启发了诸多与经典及量子计算架构互补的新型概念（如人工神经网络与神经形态计算机），但二者性能差异尚不明确。本文提出一种基于计算问题规模递增求解的认知性能基准测试新方法。我们通过人类参与者实验测定计算效率，并将其与复杂度类别进行基准对标。研究表明，具有有限视野范围并叠加噪声的神经形态架构能较好地近似实验结果。该基准测试还表明，与神经形态模型相比，人类能力尺度上未见量子优势。因此，本框架通过将人脑视为黑箱，为理解其计算效率提供了独特视角。