arXiv subjects

Di Zhu

Publications and source records attributed to Di Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Coherent control of a superconducting qubit using light

Quantum communications technologies require a network of quantum processors connected with low loss and low noise communication channels capable of distributing entangled states. Superconducting microwave qubits operating in cryogenic environments have emerged as promising candidates for quantum processor nodes. However, scaling these systems is challenging because they require bulky microwave components with high thermal loads that can quickly overwhelm the cooling power of a dilution refrigerator. Telecommunication frequency optical signals, meanwhile, can be fabricated in significantly smaller form factors while avoiding challenges due to high signal loss, noise sensitivity, and thermal loads due to their high carrier frequency and propagation in silica optical fibers. Transduction of information via coherent links between optical and microwave frequencies is therefore critical to leverage the advantages of optics for superconducting microwave qubits, while also enabling superconducting processors to be linked with low-loss optical interconnects. Here, we demonstrate coherent optical control of a superconducting qubit. We achieve this by developing a microwave-optical quantum transducer that operates with up to 1.18% conversion efficiency with low added microwave noise, and demonstrate optically-driven Rabi oscillations in a superconducting qubit.

quant-ph↗

arXiv2024

Integrated electro-optic digital-to-analog link for efficient computing and arbitrary waveform generation

The rapid growth in artificial intelligence and modern communication systems demands innovative solutions for increased computational power and advanced signaling capabilities. Integrated photonics, leveraging the analog nature of electromagnetic waves at the chip scale, offers a promising complement to approaches based on digital electronics. To fully unlock their potential as analog processors, establishing a common technological base between conventional digital electronic systems and analog photonics is imperative to building next-generation computing and communications hardware. However, the absence of an efficient interface has critically challenged comprehensive demonstrations of analog advantage thus far, with the scalability, speed, and energy consumption as primary bottlenecks. Here, we address this challenge and demonstrate a general electro-optic digital-to-analog link (EO-DiAL) enabled by foundry-based lithium niobate nanophotonics. Using purely digital inputs, we achieve on-demand generation of (i) optical and (ii) electronic waveforms at information rates up to 186 Gbit/s. The former addresses the digital-to-analog electro-optic conversion challenge in photonic computing, showcasing high-fidelity MNIST encoding while consuming 0.058 pJ/bit. The latter enables a pulse-shaping-free microwave arbitrary waveform generation method with ultrabroadband tunable delay and gain. Our results pave the way for efficient and compact digital-to-analog conversion paradigms enabled by integrated photonics and underscore the transformative impact analog photonic hardware may have on various applications, such as computing, optical interconnects, and high-speed ranging.

physics.optics↗

arXiv2024

Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

Here we show a photonic computing accelerator utilizing a system-level thin-film lithium niobate circuit which overcomes this limitation. Leveraging the strong electro-optic (Pockels) effect and the scalability of this platform, we demonstrate photonic computation at speeds up to 1.36 TOPS while consuming 0.057 pJ/OP. Our system features more than 100 thin-film lithium niobate high-performance components working synergistically, surpassing state-of-the-art systems on this platform. We further demonstrate binary-classification, handwritten-digit classification, and image classification with remarkable accuracy, showcasing our system's capability of executing real algorithms. Finally, we investigate the opportunities offered by combining our system with a hybrid-integrated distributed feedback laser source and a heterogeneous-integrated modified uni-traveling carrier photodiode. Our results illustrate the promise of thin-film lithium niobate as a computational platform, addressing current bottlenecks in both electronic and photonic computation. Its unique properties of high-performance electro-optic weight encoding and conversion, wafer-scale scalability, and compatibility with integrated lasers and detectors, position thin-film lithium niobate photonics as a valuable complement to silicon photonics, with extensions to applications in ultrafast and power-efficient signal processing and ranging.

physics.optics↗

arXiv2024

Identifying Influential nodes in Brain Networks via Self-Supervised Graph-Transformer

Studying influential nodes (I-nodes) in brain networks is of great significance in the field of brain imaging. Most existing studies consider brain connectivity hubs as I-nodes. However, this approach relies heavily on prior knowledge from graph theory, which may overlook the intrinsic characteristics of the brain network, especially when its architecture is not fully understood. In contrast, self-supervised deep learning can learn meaningful representations directly from the data. This approach enables the exploration of I-nodes for brain networks, which is also lacking in current studies. This paper proposes a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT) to identify I-nodes, which has three main characteristics. First, as a self-supervised model, SSGR-GT extracts the importance of brain nodes to the reconstruction. Second, SSGR-GT uses Graph-Transformer, which is well-suited for extracting features from brain graphs, combining both local and global characteristics. Third, multimodal analysis of I-nodes uses graph-based fusion technology, combining functional and structural brain information. The I-nodes we obtained are distributed in critical areas such as the superior frontal lobe, lateral parietal lobe, and lateral occipital lobe, with a total of 56 identified across different experiments. These I-nodes are involved in more brain networks than other regions, have longer fiber connections, and occupy more central positions in structural connectivity. They also exhibit strong connectivity and high node efficiency in both functional and structural networks. Furthermore, there is a significant overlap between the I-nodes and both the structural and functional rich-club. These findings enhance our understanding of the I-nodes within the brain network, and provide new insights for future research in further understanding the brain working mechanisms.

q-bio.NC↗

arXiv2024

Observation of in-gap states in a two-dimensional CrI2/NbSe2 heterostructure

Low-dimensional magnetic structures coupled with superconductors are promising platforms for realizing Majorana zero modes, which have potential applications in topological quantum computing. Here, we report a two-dimensional (2D) magnetic-superconducting heterostructure consisting of single-layer chromium diiodide (CrI2) on a niobium diselenide (NbSe2) superconductor. Single-layer CrI2 nanosheets, which hold antiferromagnetic (AFM) ground states by our first-principles calculations, were epitaxially grown on the layered NbSe2 substrate. Using scanning tunneling microscopy/spectroscopy, we observed robust in-gap states spatially located at the edge of the nanosheets and defect-induced zero-energy peaks inside the CrI2 nanosheets. Magnetic-flux vortices induced by an external field exhibit broken threefold rotational symmetry of pristine NbSe2 superconductor, implying the efficient modulation of the interfacial superconducting states by the epitaxial CrI2 layer. A phenomenological model suggests the existence of chiral edge states in a 2D AFM-superconducting hybrid system with an even Chern number, providing a qualitatively plausible understanding for our experimental observation.

cond-mat.supr-con↗

arXiv2024

Dielectric Fano Nanoantennas for Enabling Sub-Nanosecond Lifetimes in NV-based Single Photon Emitters

Solid-state quantum emitters are essential sources of single photons, and enhancing their emission rates is of paramount importance for applications in quantum communications, computing, and metrology. One approach is to couple quantum emitters with resonant photonic nanostructures, where the emission rate is enhanced due to the Purcell effect. Dielectric nanoantennas are promising as they provide strong emission enhancement compared to plasmonic ones, which suffer from high Ohmic loss. Here, we designed and fabricated a dielectric Fano resonator based on a pair of silicon (Si) ellipses and a disk, which supports the mode hybridization between quasi-bound-states-in-the-continuum (quasi-BIC) and Mie resonance. We demonstrated the performance of the developed resonant system by interfacing it with single photon emitters (SPEs) based on nitrogen-vacancy (NV-) centers in nanodiamonds (NDs). We observed that the interfaced emitters have a Purcell enhancement factor of ~10, with sub-ns emission lifetime and a polarization contrast of 9. Our results indicate a promising method for developing efficient and compact single-photon sources for integrated quantum photonics applications.

physics.optics↗

arXiv2024

Periodic domain inversion in single crystal barium titanate-on-insulator thin film

We report experimentally achieving first-ever electric field periodic poling of single crystal barium titanate (BTO, or BaTiO3) thin film on insulator. Owing to the outstanding optical nonlinearities of BTO, this result is a key step towards achieving quasi-phase-matching in BTO. We first grow the BTO thin film on a dysprosium scandate substrate using pulsed laser deposition with a thin layer of strontium ruthenate later serving as the bottom electrode for poling. We present characterization of the BTO thin film using x-ray diffraction and piezo-response force microscopy to clearly demonstrate single crystal, single domain growth of the film which enables the desired periodic poling. To investigate the poling quality, we apply both non-destructive piezo force response microscopy and destructive etching-assisted scanning electron microscopy and we show that high quality, uniform and intransient poling with 50 % duty cycle and periods ranging from 2 μm to 10 μm is achieved. The successful realization of periodic poling in BTO thin film unlocks the potential for highly efficient nonlinear processes under quasi-phase-matching that seemed far-fetched with prior polycrystalline BTO thin films which predominantly relied on efficiency-limited random or non-phase matching conditions and is a key step towards integration of BTO photonic devices.

physics.optics↗

arXiv2024

CMOS-compatible photonic integrated circuits on thin-film ScAlN

Scandium aluminum nitride (ScAlN) has recently emerged as an attractive material for integrated photonics due to its favorable nonlinear optical properties and compatibility with CMOS fabrication. Despite the promising and versatile material properties, it is still an outstanding challenge to realize low-loss photonic circuits on thin-film ScAlN-on-insulator wafers. Here, we present a systematic study on the material quality of sputtered thin-film ScAlN produced in a CMOS-compatible 200 mm line, and an optimized fabrication process to yield 400 nm thick, fully etched waveguides. With surface polishing and annealing, we achieve micro-ring resonators with an intrinsic quality factor as high as $1.47\times 10^5$, corresponding to a propagation loss of 2.4 dB/cm. These results serve as a critical step towards developing future large-scale, low-loss photonic integrated circuits based on ScAlN.

physics.optics↗

arXiv2024

Efficient photon-pair generation in layer-poled lithium niobate nanophotonic waveguides

Integrated photon-pair sources are crucial for scalable photonic quantum systems. Thin-film lithium niobate is a promising platform for on-chip photon-pair generation through spontaneous parametric down-conversion (SPDC). However, the device implementation faces practical challenges. Periodically poled lithium niobate (PPLN), despite enabling flexible quasi-phase matching, suffers from poor fabrication reliability and device repeatability, while conventional modal phase matching (MPM) methods yield limited efficiencies due to inadequate mode overlaps. Here, we introduce a layer-poled lithium niobate (LPLN) nanophotonic waveguide for efficient photon-pair generation. It leverages layer-wise polarity inversion through electrical poling to break spatial symmetry and significantly enhance nonlinear interactions for MPM, achieving a notable normalized second-harmonic generation (SHG) conversion efficiency of 4615% W^{-1}cm^{-2}. Through a cascaded SHG and SPDC process, we demonstrate photon-pair generation with a normalized brightness of 3.1*10^6 Hz nm^{-1} mW^{-2} in a 3.3 mm long LPLN waveguide, surpassing existing on-chip sources under similar operating configurations. Crucially, our LPLN waveguides offer enhanced fabrication reliability and reduced sensitivity to geometric variations and temperature fluctuations compared to PPLN devices. We expect LPLN to become a promising solution for on-chip nonlinear wavelength conversion and non-classical light generation, with immediate applications in quantum communication, networking, and on-chip photonic quantum information processing.

physics.optics↗

arXiv2024

Integrated electro-optics on thin-film lithium niobate

Electro-optics serves as the crucial bridge between electronics and photonics, unlocking a wide array of applications ranging from communications and computing to sensing and quantum information. Integrated electro-optics approaches in particular enable essential electronic high-speed control for photonics while offering substantial photonic parallelism for electronics. Recent strides in thin-film lithium niobate photonics have ushered revolutionary advancements in electro-optics. This technology not only offers the requisite strong electro-optic coupling but also boasts ultra-low optical loss and high microwave bandwidth. Further, its tight confinement and compatibility with nanofabrication allow for unprecedented reconfigurability and scalability, facilitating the creation of novel and intricate devices and systems that were once deemed nearly impossible in bulk systems. Building upon this platform, the field has witnessed the emergence of various groundbreaking electro-optic devices surpassing the current state of the art, and introducing functionalities that were previously non-existent. This technological leap forward provides a unique framework to explore various realms of physics as well, including photonic non-Hermitian synthetic dimensions, active topological physics, and quantum electro-optics. In this review, we present the fundamental principles of electro-optics, drawing connections between fundamental science and the forefront of technology. We discuss the accomplishments and future prospects of integrated electro-optics, enabled by thin-film lithium niobate platform.

physics.optics↗

arXiv2024

Uncover the nature of overlapping community in cities

Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities.

physics.soc-ph↗

arXiv2023

Topological Superconductivity in Two-Dimensional Altermagnetic Metals

Bringing magnetic metals into superconducting states represents an important approach for realizing unconventional superconductors and potentially even topological superconductors. Altermagnetism, classified as a third basic collinear magnetic phase, gives rise to intriguing momentum-dependent spin-splitting of the band structure, and results in an even number of spin-polarized Fermi surfaces due to the symmetry-enforced zero net magnetization. In this work, we investigate the effect of this new magnetic order on the superconductivity of a two-dimensional metal with d-wave altermagnetism and Rashba spin-orbital coupling. Specifically we consider an extended attractive Hubbard interaction, and determine the types of superconducting pairing that can occur in this system and ascertain whether they possess topological properties. Through self-consistent mean-field calculations, we find that the system in general favors a mixture of spin-singlet s-wave and spin-triplet p-wave pairings, and that the altermagnetism is beneficial to the latter. Using symmetry arguments supported by detailed calculations, we show that a number of topological superconductors, including both first-order and second-order ones, can emerge when the p-wave pairing dominates. In particular, we find that the second-order topological superconductor is enforced by a $\mathcal{C}_{4z}\mathcal{T}$ symmetry, which renders the spin polarization of Majorana corner modes into a unique entangled structure. Our study demonstrates that altermagnetic metals are fascinating platforms for the exploration of intrinsic unconventional superconductivity and topological superconductivity.

cond-mat.supr-con↗

arXiv2023

ChatGPT Informed Graph Neural Network for Stock Movement Prediction

ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier. In this research, we introduce a novel framework that leverages ChatGPT's graph inference capabilities to enhance Graph Neural Networks (GNN). Our framework adeptly extracts evolving network structures from textual data, and incorporates these networks into graph neural networks for subsequent predictive tasks. The experimental results from stock movement forecasting indicate our model has consistently outperformed the state-of-the-art Deep Learning-based benchmarks. Furthermore, the portfolios constructed based on our model's outputs demonstrate higher annualized cumulative returns, alongside reduced volatility and maximum drawdown. This superior performance highlights the potential of ChatGPT for text-based network inferences and underscores its promising implications for the financial sector.

q-fin.ST↗

arXiv2023

Engineering Perovskite Emissions via Optical Quasi-Bound-States-in-the-Continuum

Metal halide perovskite quantum dots (PQDs) have emerged as promising materials due to their exceptional photoluminescence (PL) properties. A wide range of applications could benefit from adjustable luminescence properties, while preserving the physical and chemical properties of the PQDs. Therefore, post-synthesis engineering has gained attention recently, involving the use of ion-exchange or external stimuli, such as extreme pressure, magnetic and electric fields. Nevertheless, these methods typically suffer from spectrum broadening, intensity quenching or yield multiple bands. Alternatively, photonic antennas can modify the radiative decay channel of perovskites via the Purcell effect, with the largest wavelength shift being 8 nm to date, at an expense of 5-fold intensity loss. Here, we present an optical nanoantenna array with polarization-controlled quasi-bound-states-in-the-continuum (q-BIC) resonances, which can engineer and shift the photoluminescence wavelength over a ~39 nm range and confers a 21-fold emission enhancement of FAPbI3 perovskite QDs. The spectrum is engineered in a non-invasive manner via lithographically defined antennas and the pump laser polarization at ambient conditions. Our research provides a path towards advanced optoelectronic devices, such as spectrally tailored quantum emitters and lasers.

physics.optics↗

arXiv2023

Sublattice-enriched tunability of bound states in second-order topological insulators and superconductors

Bound states at sharp corners have been widely viewed as the hallmark of two-dimensional second-order topological insulators and superconductors. In this work, we show that the existence of sublattice degrees of freedom can enrich the tunability of bound states on the boundary and hence lift the constraint on their locations. We take the Kane-Mele model with honeycomb-lattice structure to illustrate the underlying physics. With the introduction of an in-plane exchange field to the model, we find that the boundary Dirac mass induced by the exchange field has a sensitive dependence on the boundary sublattice termination. We find that the sensitive sublattice dependence can lead bound states to emerge at a specific type of boundary defects named as sublattice domain walls if the exchange field is of ferromagnetic nature, even in the absence of any sharp corner on the boundary. Remarkably, this sensitive dependence of the boundary Dirac mass on the boundary sublattice termination allows the positions of bound states to be manipulated to any place on the boundary for an appropriately-designed sample. With a further introduction of conventional s-wave superconductivity to the model, we find that, no matter whether the exchange field is ferromagnetic, antiferromagnetic, or ferrimagnetic, highly controllable Majorana zero modes can be achieved at the sublattice domain walls. Our work reshapes the understanding of boundary physics in second-order topological phases, and meanwhile opens potential avenues to realize highly controllable bound states for potential applications.

cond-mat.mes-hall↗

arXiv2023

Sub-1 Volt and High-Bandwidth Visible to Near-Infrared Electro-Optic Modulators

Integrated electro-optic (EO) modulators are fundamental photonics components with utility in domains ranging from digital communications to quantum information processing. At telecommunication wavelengths, thin-film lithium niobate modulators exhibit state-of-the-art performance in voltage-length product ($V_π$L), optical loss, and EO bandwidth. However, applications in optical imaging, optogenetics, and quantum science generally require devices operating in the visible-to-near-infrared (VNIR) wavelength range. In this work, we realize VNIR amplitude and phase modulators featuring $V_π$L's of sub-1 V$\cdot\,$cm, low optical loss, and high bandwidth EO response. Our Mach-Zehnder modulators exhibit a $V_π$L as low as 0.55 V$\cdot\,$cm at 738 nm, and EO bandwidths in excess of 35 GHz. Furthermore, we highlight the new opportunities these high-performance modulators offer by demonstrating the first integrated EO frequency combs at VNIR wavelengths, with over 50 lines and tunable spacing, and the first frequency shifting of pulsed light beyond its intrinsic bandwidth (up to 7x Fourier limit) by an EO shearing method.

physics.optics↗

arXiv2022

Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models

Interpretable machine learning plays a key role in healthcare because it is challenging in understanding feature importance in deep learning model predictions. We propose a novel framework that uses deep learning to study feature sensitivity for model predictions. This work combines sensitivity analysis with heterogeneous time-series deep learning model prediction, which corresponds to the interpretations of spatio-temporal features. We forecast county-level COVID-19 infection using the Temporal Fusion Transformer. We then use the sensitivity analysis extending Morris Method to see how sensitive the outputs are with respect to perturbation to our static and dynamic input features. The significance of the work is grounded in a real-world COVID-19 infection prediction with highly non-stationary, finely granular, and heterogeneous data. 1) Our model can capture the detailed daily changes of temporal and spatial model behaviors and achieves high prediction performance compared to a PyTorch baseline. 2) By analyzing the Morris sensitivity indices and attention patterns, we decipher the meaning of feature importance with observational population and dynamic model changes. 3) We have collected 2.5 years of socioeconomic and health features over 3142 US counties, such as observed cases and deaths, and a number of static (age distribution, health disparity, and industry) and dynamic features (vaccination, disease spread, transmissible cases, and social distancing). Using the proposed framework, we conduct extensive experiments and show our model can learn complex interactions and perform predictions for daily infection at the county level. Being able to model the disease infection with a hybrid prediction and description accuracy measurement with Morris index at the county level is a central idea that sheds light on individual feature interpretation via sensitivity analysis.

cs.LG↗

arXiv2022

Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective

Visual-Semantic Embedding (VSE) aims to learn an embedding space where related visual and semantic instances are close to each other. Recent VSE models tend to design complex structures to pool visual and semantic features into fixed-length vectors and use hard triplet loss for optimization. However, we find that: (1) combining simple pooling methods is no worse than these sophisticated methods; and (2) only considering the most difficult-to-distinguish negative sample leads to slow convergence and poor Recall@K improvement. To this end, we propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. We also introduce a strategy to dynamically select a group of negative samples to make the optimization converge faster and perform better. Experimental results on Flickr30K and MS-COCO demonstrate that a standard VSE using our pooling and optimization strategies outperforms current state-of-the-art systems (at least 1.0% on the metrics of recall) in image-to-text and text-to-image retrieval. Source code of our experiments is available at https://github.com/96-Zachary/vse_2ad.

cs.MM↗