Sensors are indispensable tools of modern life that are ubiquitously used in diverse settings ranging from smartphones and autonomous vehicles to the healthcare industry and space technology.By interfacing multiple se...Sensors are indispensable tools of modern life that are ubiquitously used in diverse settings ranging from smartphones and autonomous vehicles to the healthcare industry and space technology.By interfacing multiple sensors that collectively interact with the signal to be measured,one can go beyond the signal-to-noise ratios(SNR)attainable by the individual constituting elements.Such techniques have also been implemented in the quantum regime,where a linear increase in the SNR has been achieved via using entangled states.Along similar lines,coupled non-Hermitian systems have provided yet additional degrees of freedom to obtain better sensors via higher-order exceptional points.Quite recently,a new class of non-Hermitian systems,known as non-Hermitian topological sensors(NTOS)has been theoretically proposed.Remarkably,the synergistic interplay between non-Hermiticity and topology is expected to bestow such sensors with an enhanced sensitivity that grows exponentially with the size of the sensor network.Here,we experimentally demonstrate NTOS using a network of photonic time-multiplexed resonators in the synthetic dimension represented by optical pulses.By judiciously programming the delay lines in such a network,we realize the archetypal Hatano-Nelson model for our non-Hermitian topological sensing scheme.Our experimentally measured sensitivities for different lattice sizes confirm the characteristic exponential enhancement of NTOS.We show that this peculiar response arises due to the combined synergy between non-Hermiticity and topology,something that is absent in Hermitian topological lattices.Our demonstration of NTOS paves the way for realizing sensors with unprecedented sensitivities.展开更多
Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware.Photonics offers a promising solution by leveraging the unique properties of light.However,...Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware.Photonics offers a promising solution by leveraging the unique properties of light.However,conventional neural network architectures,which typically require dense programmable connections,pose several practical challenges for photonic realizations.To overcome these limitations,we propose and experimentally demonstrate Photonic Neural Cellular Automata(PNCA)for photonic deep learning with sparse connectivity.PNCA harnesses the speed and interconnectivity of photonics,as well as the self-organizing nature of cellular automata through local interactions to achieve robust,reliable,and efficient processing.We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification.We demonstrate binary(two-class)classification of images using as few as 3 programmable photonic parameters,achieving high experimental accuracy with the ability to also recognize out-ofdistribution data.The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of lightbased computing whilst mitigating their practical challenges.Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.展开更多
基金support from ARO Grant W911NF-23-1-0048NSF Grants No.1846273 and 1918549the Center for Sensing to Intelligence at Caltech.
文摘Sensors are indispensable tools of modern life that are ubiquitously used in diverse settings ranging from smartphones and autonomous vehicles to the healthcare industry and space technology.By interfacing multiple sensors that collectively interact with the signal to be measured,one can go beyond the signal-to-noise ratios(SNR)attainable by the individual constituting elements.Such techniques have also been implemented in the quantum regime,where a linear increase in the SNR has been achieved via using entangled states.Along similar lines,coupled non-Hermitian systems have provided yet additional degrees of freedom to obtain better sensors via higher-order exceptional points.Quite recently,a new class of non-Hermitian systems,known as non-Hermitian topological sensors(NTOS)has been theoretically proposed.Remarkably,the synergistic interplay between non-Hermiticity and topology is expected to bestow such sensors with an enhanced sensitivity that grows exponentially with the size of the sensor network.Here,we experimentally demonstrate NTOS using a network of photonic time-multiplexed resonators in the synthetic dimension represented by optical pulses.By judiciously programming the delay lines in such a network,we realize the archetypal Hatano-Nelson model for our non-Hermitian topological sensing scheme.Our experimentally measured sensitivities for different lattice sizes confirm the characteristic exponential enhancement of NTOS.We show that this peculiar response arises due to the combined synergy between non-Hermiticity and topology,something that is absent in Hermitian topological lattices.Our demonstration of NTOS paves the way for realizing sensors with unprecedented sensitivities.
基金support from ARO grant no.W911NF-23-1-0048,NSF grant no.1846273 and 1918549Center for Sensing to Intelligence at Caltech,and NASA/JPL。
文摘Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware.Photonics offers a promising solution by leveraging the unique properties of light.However,conventional neural network architectures,which typically require dense programmable connections,pose several practical challenges for photonic realizations.To overcome these limitations,we propose and experimentally demonstrate Photonic Neural Cellular Automata(PNCA)for photonic deep learning with sparse connectivity.PNCA harnesses the speed and interconnectivity of photonics,as well as the self-organizing nature of cellular automata through local interactions to achieve robust,reliable,and efficient processing.We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification.We demonstrate binary(two-class)classification of images using as few as 3 programmable photonic parameters,achieving high experimental accuracy with the ability to also recognize out-ofdistribution data.The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of lightbased computing whilst mitigating their practical challenges.Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.
