Universal self-correcting computing with disordered exciton-polariton neural networks

• Main Authors: Xu, Huawen, Ghosh, Sanjib, Matuszewski, Michal, Liew, Timothy Chi Hin
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2020
• Subjects: Science::Physics; Exciton-polaritons; Neural Networks
• Online Access: https://hdl.handle.net/10356/143009
• Institution: Nanyang Technological University

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.