This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

翻译：本文证明，人类决策中的一些非经典模型可以成功地在量子计算机上以电路形式运行。自20世纪60年代以来，许多观察到的认知行为已被证明违反了基于经典概率和集合论的规则。例如，调查中问题的提问顺序会影响参与者回答“是”或“否”的决策，因此对两个问题均回答“是”的人群无法被建模为两个固定集合的交集。然而，该现象可以通过不同顺序执行的投影序列进行建模。这一示例及其他案例已借助量子概率理论成功描述，该理论依赖于比较子空间之间的夹角而非子集之间的体积。如今在21世纪20年代初，量子计算机的发展已使得部分量子认知模型能够通过量子硬件实现与研究，具体方法是将心理状态表示于量子比特寄存器中，而认知操作与决策则利用不同的量子门与测量操作实现。本文针对量子认知模型开发了此类量子电路表示，尤其聚焦于对顺序效应及不确定性决策的建模。本文并非宣称人脑实际使用量子比特和量子电路（正如布尔集合论的应用并不要求大脑使用经典比特），而是指出量子认知与量子计算共享的数学基础推动了利用量子计算机进行认知建模的探索。关键的量子特性包括叠加、纠缠与坍缩，这些数学要素为认知模型、量子硬件与电路实现提供了共同语言。