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Integrated photonics provides an important platform for simulating physical models with high-performance chip-scale devices, where the lattice size and the time-dependence of a model are key ingredients for further enriching the functionality of a photonic chip. Here, we propose and demonstrate the construction of various time-dependent Hamiltonian models using a single microresonator on thin-film lithium niobate chip. Such an integrated microresonator holds high quality factor to 10^6, and supports the construction of the synthetic frequency lattice with effective lattice sites up to 152 under the electro-optic modulation. By further applying a bichromatic modulation composed of two radio-frequency signals oppositely detuned from the resonant frequency in the microresonator, we build different time-dependent Hamiltonians with the time-varying nearest-neighbor coupling strength in synthetic frequency lattice. We measure the temporal features from capturing the dynamic band structures of the lattice and demonstrate a variety of time-dependent synthetic lattice models by engineering the driven pattern of the modulation, highlighting great flexibility of the microresonator. Our work shows a photonic chip for simulating versatile time-dependent Hamiltonians, which pushes forward quantum simulations in integrated photonics with great experimental tunability and reconfigurability., Comment: 14 pages, 5 figures

Optical frequency combs (OFCs), composed of equally spaced frequency tones, have spurred advancements in communications, spectroscopy, precision measurement and fundamental physics research. A prevalent method for generating OFCs involves the electro-optic (EO) effect, i.e., EO comb, renowned for its rapid tunability via precise microwave field control. Recent advances in integrated lithium niobate (LN) photonics have greatly enhanced the efficiency of EO effect, enabling the generation of broadband combs with reduced microwave power. However, parasitic nonlinear effects, such as Raman scattering and four-wave mixing, often emerge in high quality nonlinear devices, impeding the expansion of comb bandwidth and the minimization of frequency noise. Here, we tame these nonlinear effects and present a novel type of OFC, i.e., the self-locked Raman-electro-optic (REO) microcomb by leveraging the collaboration of EO, Kerr and Raman scattering processes. The spectral width of the REO microcomb benefits from the Raman gain and Kerr effect, encompassing nearly 1400 comb lines spanning over 300 nm with a fine repetition rate of 26.03 GHz, much larger than the pure EO combs. Remarkably, the system can maintain a self-locked low-noise state in the presence of multiple nonlinearities without the need for external active feedback. Our approach points to a direction for improving the performance of microcombs and paves the way for exploring new nonlinear physics, such as new laser locking techniques, through the collaboration of inevitable multiple nonlinear effects in integrated photonics.

Generated in high-Q optical microresonators, dissipative Kerr soliton microcombs constitute broadband optical frequency combs with chip sizes and repetition rates in the microwave to millimeter-wave range. For frequency metrology applications such as spectroscopy, optical atomic clocks and frequency synthesizers, octave-spanning soliton microcombs generated in dispersion optimized microresonator are required, which allow self-referencing for full frequency stabilization. In addition, field-deployable applications require the generation of such soliton microcombs simple, deterministic, and reproducible. Here, we demonstrate a novel scheme to generate self-emerging solitons in integrated lithium niobate microresonators. The single soliton features a broadband spectral bandwidth with dual dispersive waves, allowing 2f-3f self-referencing. Via harnessing the photorefractive effect of lithium niobate to significantly extend the soliton existence range, we observe a spontaneous yet deterministic single-soliton formation. The soliton is immune to external perturbation and can operate continuously over 13 hours without active feedback control. Finally, via integration with a pre-programed DFB laser, we demonstrate turnkey soliton generation. With further improvement of microresonator Q and hybrid integration with chip-scale laser chips, compact soliton microcomb devices with electronic actuation can be created, which can become central elements for future LiDAR, microwave photonics and optical telecommunications., Comment: 9 pages, 4 figures

For the applications of the frequency comb in microresonators, it is essential to obtain a fully frequency-stabilized microcomb laser source. Here, we demonstrate an atom-referenced stabilized soliton microcomb generation system based on the integrated microring resonator. The pump light around $1560.48\,\mathrm{nm}$ locked to an ultra-low-expansion (ULE) cavity, is frequency-doubled and referenced to the atomic transition of $^{87}\mathrm{Rb}$. The repetition rate of the soliton microcomb is injection-locked to an atomic-clock-stabilized radio frequency (RF) source, leading to mHz stabilization at $1$ seconds. As a result, all comb lines have been frequency-stabilized based on the atomic reference and could be determined with very high precision reaching $\sim18\,\mathrm{Hz}$ at 1 second, corresponding to the frequency stability of $9.5\times10^{-14}$. Our approach provides an integrated and fully stabilized microcomb experiment scheme with no requirement of $f-2f$ technique, which could be easily implemented and generalized to various photonic platforms, thus paving the way towards the portable and ultraprecise optical sources for high precision spectroscopy., Comment: 7 pages, 8 figures

In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model. The attention module exploits global receptive fields that can capture and align long-range correlations between consecutive frames, which can be beneficial for enhancing perceptual quality of videos. The frame to be enhanced is fed into the deep network together with its neighboring frames, and in the first stage features at different depths are extracted. Then extracted features are fed into attention blocks to explore global temporal correlations, followed by a series of upsampling and convolution layers. Finally, the resulting features are processed by the QP-conditional adaptation module which leverages the corresponding QP information. In this way, a single model can be used to enhance adaptively to various QPs without requiring multiple models specific for every QP value, while having similar performance. Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.

Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands. In addition, models are usually optimized for a single RD tradeoff. Recent slimmable image codecs can dynamically adjust their model capacity to gracefully reduce the memory and computation requirements, without harming RD performance. In this paper we propose a slimmable video codec (SlimVC), by integrating a slimmable temporal entropy model in a slimmable autoencoder. Despite a significantly more complex architecture, we show that slimming remains a powerful mechanism to control rate, memory footprint, computational cost and latency, all being important requirements for practical video compression., Comment: Computer Vision and Pattern Recognition Workshop(CLIC2022)

In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms., Comment: 5 pages, 4 figures

Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the relationship between the luma and chroma components. Recently it has been proven that advanced cross-component prediction methods based on Neural Networks (NN) can bring additional coding gains. In this paper, spatial information refinement is proposed for improving NN-based chroma intra prediction. Specifically, the performance of chroma intra prediction can be improved by refined down-sampling or by incorporating location information. Experimental results show that the two proposed methods obtain 0.31%, 2.64%, 2.02% and 0.33%, 3.00%, 2.12% BD-rate reduction on Y, Cb and Cr components, respectively, under All-Intra configuration, when implemented in Versatile Video Coding (H.266/VVC) test model. Index Terms-Chroma intra prediction, convolutional neural networks, spatial information refinement.

Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this paper, we introduce deep video compression with perceptual optimizations (DVC-P), which aims at increasing perceptual quality of decoded videos. Our proposed DVC-P is based on Deep Video Compression (DVC) network, but improves it with perceptual optimizations. Specifically, a discriminator network and a mixed loss are employed to help our network trade off among distortion, perception and rate. Furthermore, nearest-neighbor interpolation is used to eliminate checkerboard artifacts which can appear in sequences encoded with DVC frameworks. Thanks to these two improvements, the perceptual quality of decoded sequences is improved. Experimental results demonstrate that, compared with the baseline DVC, our proposed method can generate videos with higher perceptual quality achieving 12.27% reduction in a perceptual BD-rate equivalent, on average.

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wideband transmission spectra that consist of multiple resonant modes. It is different from the previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range., Comment: 9 pages, 7 figures

Dissipative Kerr soliton offers broadband coherent and low-noise frequency comb and stable temporal pulse train, having shown great potential applications in spectroscopy, communications, and metrology. Breathing soliton is a particular dissipative Kerr soliton that the pulse duration and peak intensity show periodic oscillation. However, the noise and stability of the breathing soliton is still remaining unexplored, which would be the main obstacle for future applications. Here, we have investigated the breathing dissipative Kerr solitons in the silicon nitride (SiN) microrings, while the breather period shows uncertainties around MHz in both simulation and experiments. By applying a modulated pump, the breathing frequency can be injectively locked to the modulation and tuned over tens of MHz with frequency noise significantly suppressed. Our demonstration offers an alternative knob for the controlling of soliton dynamics in microresonator and paves a new avenue towards practical applications of breathing soliton.

The coherent temporal soliton in optical microresonators has attracted great attention recently. Here, we demonstrate the dissipative Kerr soliton generation in a microrod resonator, by utilizing an auxiliary-laser-assisted thermal response control. By external stress tuning, the repetition rate of the soliton has been controlled over a large range of 30 MHz. Our platform promises precise tuning and locking of the repetition frequency of coherent mode-locked comb in the microresonator, and holds great potential for applications in spectroscopy and precision measurements., Comment: 5 Pages, 4 figures