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Abstract 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. 

Abstract 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-of-distribution 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 light-based 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. 

Lithium niobate (LiNbO3, LN) is a ferroelectric crystal of interest for integrated photonics owing to its large second-order optical nonlinearity and the ability to impart periodic poling via an external electric field. However, on-chip device performance based on thin-film lithium niobate (TFLN) is presently limited by propagation losses arising from surface roughness and corrugations. Atomic layer etching (ALE) could potentially smooth these features and thereby increase photonic performance, but no ALE process has been reported for LN. Here, we report an isotropic ALE process for x-cut MgO-doped LN using sequential exposures of H2 and SF6/Ar plasmas. We observe an etch rate of 1.59±0.02 nm/cycle with a synergy of 96.9%. We also demonstrate that ALE can be achieved with SF6/O2 or Cl2/BCl3 plasma exposures in place of the SF6/Ar plasma step with synergies of 99.5% and 91.5%, respectively. The process is found to decrease the sidewall surface roughness of TFLN waveguides etched by physical Ar+ milling by 30% without additional wet processing. Our ALE process could be used to smooth sidewall surfaces of TFLN waveguides as a postprocessing treatment, thereby increasing the performance of TFLN nanophotonic devices and enabling new integrated photonic device capabilities. 

Abstract Photonics offers unique capabilities for quantum information processing (QIP) such as room-temperature operation, the scalability of nanophotonics, and access to ultrabroad bandwidths and consequently ultrafast operation. Ultrashort pulse sources of quantum states in nanophotonics are an important building block for achieving scalable ultrafast QIP; however, their demonstrations so far have been sparse. Here, we demonstrate a femtosecond biphoton source in dispersion-engineered periodically poled lithium niobate nanophotonics. We measure 17 THz of bandwidth for the source centered at 2.09 µm, corresponding to a few optical cycles, with a brightness of 8.8 GHz/mW. Our results open new paths toward realization of ultrafast nanophotonic QIP. 

Traditional absorption spectroscopy has a fundamental difficulty in resolving small absorbance from a strong background due to the instability of laser sources. Existing background-free methods in broadband vibrational spectroscopy help to alleviate this problem but face challenges in realizing either low extinction ratios or time-resolved field measurements. Here, we introduce optical-parametric-amplification-enhanced background-free spectroscopy, in which the excitation background is first suppressed by an interferometer, and then the free-induction decay that carries molecular signatures is selectively amplified. We show that this method can improve the limit of detection in linear interferometry by order(s) of magnitude without requiring lower extinction ratios or a time-resolved measurement, which can benefit sensing applications in detecting trace species. 

We investigate a resource-efficient distributed quantum sensing (DQS) scheme using phase-sensitive optical parametric amplifiers and linear optics, achieving sensitivity levels close to the optimal limit determined by the quantum Fisher information of the resource state. 

We theoretically describe and experimentally observe signatures of spontaneous topological soliton formation through the locking of domain walls in a quadratic nonlinear resonator. These dark pulses can have a temporal duration of 65 fs. 

We introduce and experimentally demonstrate a FROG-based ultrashort pulse characterization technique using nanophotonic parametric amplification as a crucial tool for ultrafast nanophotonic circuits, and measure sub-50-femtosecond pulses. 

We experimentally demonstrate an end-to-end all-optical recurrent neural net-work utilizing short laser pulses and ultrafastχ⁽²⁾nonlinear optics to unlock unprecedented computational clock rates and massively parallel information processing for time-series classification and prediction tasks.