arXiv subjects

Ruizhe Zhao

Publications and source records attributed to Ruizhe Zhao.

15 recordsLinked to original sources

arXiv2026

Cascaded Metasurface Interferometer for Multipath Interference with Classical and Quantum Light

Beamsplitters represent fundamental components in both classical and quantum optical systems, enabling the distribution of light, as well as the generation of interference, superposition and entanglement. However, optical networks constructed from conventional bulk 2x2-beamsplitters encounter inherent scalability issues, as the number of required beamsplitters scales quadratically with the number of optical modes for a fully connected network. Metasurfaces offer a promising route to overcome these constraints. By manipulating light at the wavelength scale compact optical components with advanced functionalities can be constructed, which address several modes simultaneously. In this work, we design and experimentally utilize a metasurface as a multiport beamsplitter. Furthermore, we realize a multimode interferometer composed of two cascaded metasurfaces. We characterize the individual and cascaded metasurfaces using classical light, showing controllable splitting ratios through tunable phase relations. We then expand the approach to quantum light, employing single photons to demonstrate second- and third-order photon correlations, as well as single photon interference across multiple spatial paths. These results establish metasurface-based multiport beamsplitters as a scalable and reconfigurable platform bridging classical and quantum photonics.

physics.optics↗

arXiv2024

Energy loss of a heavy fermion in a collisional QED plasma

We compute the energy loss of heavy fermions moving in a plasma, taking into account the modification of the photon collective modes induced by collisions using a Bhatnagar-Gross-Krook collisional kernel. We include contributions from both hard and soft scatterings of the heavy fermion using a collisionally modified hard-thermal-loop resummed propagator. Using this method, one does not need to introduce a separation scale between hard- and soft-momentum exchanges. To place our calculation in context, we review other theoretical approaches to computing the collisional energy loss of fermions and discuss the systematics and results obtained in each approach compared to using a resummed propagator for both hard and soft momentum exchanges. Our final results indicate that self-consistently including the effect of collisions in the self-energies of the resummed propagator results in an increased energy loss compared to using collisionless hard-thermal-loop propagators. The effect becomes larger as the magnitude of the coupling constant and the velocity of the fermion increase.

hep-ph↗

arXiv2023

Collective modes of a collisional anisotropic quark-gluon plasma

In this paper we consider the collective modes of a momentum-space anisotropic quark-gluon plasma taking into account the effect of collisions between the plasma constituents. Our analysis is carried out using a collisional kernel of Bhatnagar-Gross-Krook form and extends prior analyses in the literature by considering all possible angles of propagation of the gluonic modes relative to the momentum-anisotropy axis. We extract both the stable and unstable modes as a function of the collision rate and confirm prior findings that gluonic unstable modes can be eliminated from the spectrum if the collision rate is sufficiently large. In addition, we discuss the conditions necessary for the existence of unstable modes and present evidence that unstable mode growth rates are maximal for modes with momentum along the anisotropy direction. Finally, we demonstrate that when there is a finite collisional rate, gluonic unstable modes are absent from the spectrum at both small and large momentum anisotropy. These results pave the way for understanding the impact of collisions on a variety of non-equilibrium quark-gluon plasma observables.

hep-ph↗

arXiv2021

Phism: Polyhedral High-Level Synthesis in MLIR

Polyhedral optimisation, a methodology that views nested loops as polyhedra and searches for their optimal transformation regarding specific objectives (parallelism, locality, etc.), sounds promising for mitigating difficulties in automatically optimising hardware designs described by high-level synthesis (HLS), which are typically software programs with nested loops. Nevertheless, existing polyhedral tools cannot meet the requirements from HLS developers for platform-specific customisation and software/hardware co-optimisation. This paper proposes $ϕ_{sm}$ (phism), a polyhedral HLS framework built on MLIR, to address these challenges through progressive lowering multi-level intermediate representations (IRs) from polyhedra to HLS designs.

cs.PL↗

arXiv2020

Polarization-Encrypted Orbital Angular Momentum Multiplexed Metasurface Holography

Metasurface holography has the advantage of realizing complex wavefront modulation by thin layers together with the progressive technique of computer-generated holographic imaging. Despite the well-known light parameters, like amplitude, phase, polarization and frequency, the orbital angular momentum (OAM) of a beam can be regarded as another degree of freedom. Here, we propose and demonstrate orbital angular momentum multiplexing at different polarization channels using a birefringent metasurface for holographic encryption. The OAM selective holographic information can only be reconstructed with the exact topological charge and a specific polarization state. By using an incident beam with different topological charges as erasers, we mimic a super-resolution case for the reconstructed image, in analogy to the well-known STED technique in microscopy. The combination of multiple polarization channels together with the orbital angular momentum selectivity provides a higher security level for holographic encryption. Such a technique can be applied for beam shaping, optical camouflage, data storage, and dynamic displays.

physics.optics↗

arXiv2020

Nonlinear wavefront control by geometric-phase dielectric metasurfaces: Influence of mode field and rotational symmetry

Nonlinear Pancharatnam-Berry phase metasurfaces facilitate the nontrivial phase modulation for frequency conversion processes by leveraging photon-spin dependent nonlinear geometric-phases. However, plasmonic metasurfaces show some severe limitation for nonlinear frequency conversion due to the intrinsic high ohmic loss and low damage threshold of plasmonic nanostructures. Here, we systematically study the nonlinear geometric-phases associated with the third-harmonic generation process occurring in all-dielectric metasurfaces, which are composed of silicon nanofins with different in-plane rotational symmetries. We find that the wave coupling among different field components of the resonant fundamental field gives rise to the appearance of different nonlinear geometric-phases of the generated third-harmonic signals. The experimental observations of the nonlinear beam steering and nonlinear holography realized in this work by all-dielectric geometric-phase metasurfaces are well explained with our developed theory. Our work offers a new physical picture to understand the nonlinear optical process occurring at nanoscale dielectric resonators and will help in the design of nonlinear metasurfaces with tailored phase properties.

physics.optics↗

arXiv2020

Silicon metasurfaces for third harmonic geometric phase manipulation and multiplexed holography

Nonlinear wavefront control is a crucial requirement in realizing nonlinear optical applications with metasurfaces. Numerous aspects of nonlinear frequency conversion and wavefront control have been demonstrated for plasmonic metasurfaces. However, several disadvantages limit their applicability in nonlinear nanophotonics, including high dissipative loss and low optical damage threshold. In contrast, it has been shown that metasurfaces made of high-index dielectrics can provide strong nonlinear responses. Regardless of the recent progress in nonlinear optical processes using all-dielectric nanostructures and metasurfaces, much less advancement has been made in realizing a full wavefront control directly with the generation process. Here, we demonstrate the nonlinear wavefront control for the third-harmonic generation with a silicon metasurface. We use a Pancharatnam-Berry phase approach to encode phase gradients and holographic images on nanostructured silicon metasurfaces. We experimentally demonstrate the polarization-dependent wavefront control and the reconstruction of an encoded hologram at the third-harmonic wavelength with high fidelity. Further, we show that holographic multiplexing is possible by utilizing the polarization states of the third harmonic generation. Our approach eases design and fabrication processes and paves the way to an easy to use toolbox for nonlinear optical wavefront control with all-dielectric metasurfaces.

physics.optics↗

arXiv2020

Second-harmonic imaging of plasmonic Pancharatnam-Berry phase metasurfaces coupled to monolayers of WS2

The nonlinear processes of frequency conversion like second harmonic generation (SHG) usually obey certain selection rules, resulting from the preservation of different kind of physical quantities, e.g., the angular momentum. For SHG created by monolayer of transition-metal dichalcogenides (TMDCs) like WS2, the valley-exciton locked selection rule predicts an SHG signal in the cross-polarization state. By combining plasmonic nanostructures with a monolayer of TMDC, a hybrid metasurface is realized which affects this nonlinear process due to an additional polarization conversion process. Here, we observe that the plasmonic metasurface modifies the light-matter interaction with the TMDC resulting in an SHG signal that is co-polarized with respect to the incident field, which is usually forbidden for solely monolayers of TMDC. We fabricate such hybrid metasurfaces by placing plasmonic nanorods on top of a monolayer WS2 and study the valley-exciton locked SHG emission from such system for different parameters, such as wavelength and polarization. Furthermore, we show the potential of the hybrid metasurface for tailoring nonlinear processes by adding additional phase information to the SHG signal using the Pancharatnam-Berry phase effect. This allows direct tailoring of the SHG emission to the far-field.

physics.optics↗

arXiv2020

Dynamic control of mode modulation and spatial multiplexing using hybrid metasurfaces

Designing reconfigurable metasurfaces that can dynamically control scattered electromagnetic waves and work in the near-infrared (NIR) and optical regimes remains a challenging task, which is hindered by the static material property and fixed structures. Phase change materials (PCMs) can provide high contrast optical refractive indexes at high frequencies between amorphous and crystal states, therefore are promising as feasible materials for reconfigurable metasurfaces. Here, we propose a hybrid metasurface that can arbitrarily modulate the complex amplitude of incident light with uniform amplitude and full $2π$ phase coverage by utilizing composite concentric rings (CCRs) with different ratios of gold and PCMs. Our designed metasurface possesses a bi-functionality that is capable of splitting beams or generating vortex beams by thermal switching between metal and semiconductor states of vanadium oxide (VO2), respectively. It can be easily integrated into low loss photonic circuits with an ultra-small footprint. Our metadevice serves as a novel paradigm for active control of beams, which may open new opportunities for signal processing, memory storage, holography, and anti-counterfeiting.

physics.optics↗

arXiv2019

Efficient Structured Pruning and Architecture Searching for Group Convolution

Efficient inference of Convolutional Neural Networks is a thriving topic recently. It is desirable to achieve the maximal test accuracy under given inference budget constraints when deploying a pre-trained model. Network pruning is a commonly used technique while it may produce irregular sparse models that can hardly gain actual speed-up. Group convolution is a promising pruning target due to its regular structure; however, incorporating such structure into the pruning procedure is challenging. It is because structural constraints are hard to describe and can make pruning intractable to solve. The need for configuring group convolution architecture, i.e., the number of groups, that maximises test accuracy also increases difficulty. This paper presents an efficient method to address this challenge. We formulate group convolution pruning as finding the optimal channel permutation to impose structural constraints and solve it efficiently by heuristics. We also apply local search to exploring group configuration based on estimated pruning cost to maximise test accuracy. Compared to prior work, results show that our method produces competitive group convolution models for various tasks within a shorter pruning period and enables rapid group configuration exploration subject to inference budget constraints.

cs.CV↗

arXiv2019

Adaptive Loss Scaling for Mixed Precision Training

Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy compared to the existing state-of-the-art MPT and single-precision floating point

cs.LG↗

arXiv2019

Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going

Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have become a hot topic. Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency. Application-tailored accelerators, when co-designed with approximation-based network training methods, transform large, dense and computationally expensive networks into small, sparse and hardware-efficient alternatives, increasing the feasibility of network deployment. In this article, we provide a comprehensive evaluation of approximation methods for high-performance network inference along with in-depth discussion of their effectiveness for custom hardware implementation. We also include proposals for future research based on a thorough analysis of current trends. This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope to inspire exciting new developments in the field.

cs.CV↗

arXiv2019

Nanoscale polarization manipulation and encryption based on dielectric metasurfaces

Manipulating the polarization of light is highly desired for versatile applications ranging from super resolution, optical trapping, to particle acceleration. The enormous freedom in metasurface design motivates the implementation of polarization control in ultrathin and compact optical systems. However, the majority of proposed strategies based on metasurfaces have been demonstrated only a spatially homogeneous polarization generation, while less attention has been devoted to spatially variant inhomogeneous vector beams. Here, we demonstrate a novel method for generating arbitrary radial and azimuthal polarization beams with high efficiencies of up to 80% by utilizing transmission-type dielectric metasurfaces. Polarization conversion metasurfaces are suitable candidates for the implementation of polarization encryption, which we demonstrate by encoding a hidden image into the spatial polarization distribution. In addition, we show that the image pattern can be modified by appropriate polarization selection of the transmitted light. Such a method may provide a practical technique for a variety of applications such as imaging, encryption and anti-counterfeiting.

physics.optics↗

arXiv2019

Multichannel vectorial holographic display and encryption

Since its invention, holography has emerged as a powerful tool to fully reconstruct the wavefronts of light including all the fundamental properties (amplitude, phase, polarization, wave vector, and frequency). For exploring the full capability for information storage/display and enhancing the encryption security of metasurface holograms, smart multiplexing techniques together with suitable metasurface designs are highly demanded. Here, we integrate multiple polarization manipulation channels for various spatial phase profiles into a single birefringent vectorial hologram by completely avoiding unwanted cross-talk. Multiple independent target phase profiles with quantified phase relations that can process significantly different information in different polarization states are realized within a single metasurface. For our metasurface holograms, we demonstrate high fidelity, large efficiency, broadband operation, and a total of twelve polarization channels. Such multichannel polarization multiplexing can be used for dynamic vectorial holographic display and can provide triple protection for optical security. The concept is appealing for applications of arbitrary spin to angular momentum conversion and various phase modulation/beam shaping elements.

physics.optics↗

arXiv2018

Towards Efficient Convolutional Neural Network for Domain-Specific Applications on FPGA

FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years. Most CNN applications on FPGA are domain-specific, e.g., detecting objects from specific categories, in which commonly-used CNN models pre-trained on general datasets may not be efficient enough. This paper presents TuRF, an end-to-end CNN acceleration framework to efficiently deploy domain-specific applications on FPGA by transfer learning that adapts pre-trained models to specific domains, replacing standard convolution layers with efficient convolution blocks, and applying layer fusion to enhance hardware design performance. We evaluate TuRF by deploying a pre-trained VGG-16 model for a domain-specific image recognition task onto a Stratix V FPGA. Results show that designs generated by TuRF achieve better performance than prior methods for the original VGG-16 and ResNet-50 models, while for the optimised VGG-16 model TuRF designs are more accurate and easier to process.

cs.CV↗