{'fraction_non_alphanumeric': 0.018867924528301886, 'fraction_numerical': 0.0, 'mean_word_length': 5.754545454545455, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Universal Self-Correcting Computing with Disordered Exciton-Polariton Neural Networks

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.