We show theoretically that neural networks based on disordered exciton-polariton systems allow the realization of Toffoli gates. Noise in input signals is self-corrected by the networks, such that the obtained Toffoli gates are in principle cascadable, where their universality would allow for arbitrary circuits without the need of additional error-correcting codes. We further find that the exciton-polariton reservoir computers can directly simulate composite circuits, such that they are a highly efficient platform allowing circuits to operate in a single step, minimizing the delay of signal transport between elements and error-correction overhead.

中文翻译：

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无序激子-极点神经网络的通用自校正计算

我们从理论上证明基于无序激子-极化子系统的神经网络允许实现Toffoli门。输入信号中的噪声通过网络进行自我校正，因此获得的Toffoli门原则上是可级联的，它们的通用性允许任意电路，而无需其他纠错码。我们进一步发现，激子-极化子储层计算机可以直接模拟复合电路，因此它们是一个高效的平台，允许电路在单个步骤中运行，从而最大程度地减少了元件之间信号传输的延迟和纠错开销。