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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. 

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. 

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. 

Efforts to reach net zero targets by the second half of the century will have profound materials supply implications. The anticipated scale and speed of the energy transition in both transportation and energy storage raises the question of whether we risk running out of the essential critical materials needed to enable this transition. Early projections suggest that disruptions are likely to occur in the short term for select critical materials, but at the same time these shortages provide a powerful incentive for the market to respond in a variety of ways before supply-level stress becomes dire. In April 2023, the MRS Focus on Sustainability subcommittee sponsored a panel discussion on the role of innovation in materials science and engineering in supporting supply chains for clean energy technologies. Drawing on examples from the panel discussion, this perspective examines the myth of materials scarcity, explains the compelling need for innovation in materials in helping supply chains dynamically adapt over time, and illustrates how the Materials Research Society is facilitating engagement with industry to support materials innovation, now and in the future.mIn this commentary, we examine the myth of materials scarcity, explain the compelling need for innovation in materials in helping supply chains dynamically adapt over time, and show how the materials research community can effectively engage with industry, policymakers, and funding agencies to drive the needed innovation in critical areas. Demand for certain materials used in clean energy technologies is forecasted to increase by multiples of current production over the next decades. This has drawn attention to supply chain risks and has created a myth that we will “run out” out of certain materials during the energy transition. The reality is that markets have multiple mechanisms to adapt over the long-term, and near-term shortages or expectations of shortages provide a powerful incentive for action. In this commentary, we highlight different ways materials innovation can help solve these issues in the near term and long term, and how the materials research community can effectively engage with industry and policymakers. 

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. 

Experience management (EM) agents in multiplayer serious games face unique challenges and responsibilities regarding the fair treatment of players. One such challenge is the Greedy Bandit Problem that arises when using traditional Multi-Armed Bandits (MABs) as EM agents, which results in some players routinely prioritized while others may be ignored. We will show that this problem can be a cause of player non-adherence in a multiplayer serious game played by human users. To mitigate this effect, we propose a new bandit strategy, the Shapley Bandit, which enforces fairness constraints in its treatment of players based on the Shapley Value. We evaluate our approach via simulation with virtual players, finding that the Shapley Bandit can be effective in providing more uniform treatment of players while incurring only a slight cost in overall performance to a typical greedy approach. Our findings highlight the importance of fair treatment among players as a goal of multiplayer EM agents and discuss how addressing this issue may lead to more effective agent operation overall. The study contributes to the understanding of player modeling and EM in serious games and provides a promising approach for balancing fairness and engagement in multiplayer environments. 

Abstract Dual-comb spectroscopy has been proven beneficial in molecular characterization but remains challenging in the mid-infrared region due to difficulties in sources and efficient photodetection. Here we introduce cross-comb spectroscopy, in which a mid-infrared comb is upconverted via sum-frequency generation with a near-infrared comb of a shifted repetition rate and then interfered with a spectral extension of the near-infrared comb. We measure CO 2 absorption around 4.25 µm with a 1-µm photodetector, exhibiting a 233-cm −1 instantaneous bandwidth, 28000 comb lines, a single-shot signal-to-noise ratio of 167 and a figure of merit of 2.4 × 10 6 Hz 1/2 . We show that cross-comb spectroscopy can have superior signal-to-noise ratio, sensitivity, dynamic range, and detection efficiency compared to other dual-comb-based methods and mitigate the limits of the excitation background and detector saturation. This approach offers an adaptable and powerful spectroscopic method outside the well-developed near-IR region and opens new avenues to high-performance frequency-comb-based sensing with wavelength flexibility.