Quantum-activated neural reservoirs on-chip open up large hardware security models for resilient authentication
<|reference_start|>Quantum-activated neural reservoirs on-chip open up large hardware security models for resilient authentication: Quantum artificial intelligence is a frontier of artificial intelligence research, pioneering quantum AI-powered circuits to address problems beyond the reach of deep learning with classical architectures. This work implements a large-scale quantum-activated recurrent neural network possessing more than 3 trillion hardware nodes/cm$^2$, originating from repeatable atomic-scale nucleation dynamics in an amorphous material integrated on-chip, controlled with 0.07 nW electric power per readout channel. Compared to the best-performing reservoirs currently reported, this implementation increases the scale of the network by two orders of magnitude and reduces the power consumption by six, reaching power efficiencies in the range of the human brain, dissipating 0.2 nW/neuron. When interrogated by a classical input, the chip implements a large-scale hardware security model, enabling dictionary-free authentication secure against statistical inference attacks, including AI's present and future development, even for an adversary with a copy of all the classical components available. Experimental tests report 99.6% reliability, 100% user authentication accuracy, and an ideal 50% key uniqueness. Due to its quantum nature, the chip supports a bit density per feature size area three times higher than the best technology available, with the capacity to store more than $2^{1104}$ keys in a footprint of 1 cm$^2$. Such a quantum-powered platform could help counteract the emerging form of warfare led by the cybercrime industry in breaching authentication to target small to large-scale facilities, from private users to intelligent energy grids.<|reference_end|>
@article{he2024quantum-activated,
    title={Quantum-activated neural reservoirs on-chip open up large hardware
  security models for resilient authentication},
    author={Zhao He, Maxim S. Elizarov, Ning Li, Fei Xiang, and Andrea Fratalocchi},
    journal={arXiv preprint arXiv:2403.14188},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14188},
    primaryClass={cond-mat.dis-nn cs.AI cs.CR}
}