arXiv subjects

Changhao Li

Publications and source records attributed to Changhao Li.

At least 73 records · Page 4Linked to original sources

arXiv2023

Can ChatGPT be used to generate scientific hypotheses?

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of "hypothesis machines", challenged by automated experimentation and adversarial peer reviews.

cs.CL↗

arXiv2023

Masked Motion Encoding for Self-Supervised Video Representation Learning

How to learn discriminative video representation from unlabeled videos is challenging but crucial for video analysis. The latest attempts seek to learn a representation model by predicting the appearance contents in the masked regions. However, simply masking and recovering appearance contents may not be sufficient to model temporal clues as the appearance contents can be easily reconstructed from a single frame. To overcome this limitation, we present Masked Motion Encoding (MME), a new pre-training paradigm that reconstructs both appearance and motion information to explore temporal clues. In MME, we focus on addressing two critical challenges to improve the representation performance: 1) how to well represent the possible long-term motion across multiple frames; and 2) how to obtain fine-grained temporal clues from sparsely sampled videos. Motivated by the fact that human is able to recognize an action by tracking objects' position changes and shape changes, we propose to reconstruct a motion trajectory that represents these two kinds of change in the masked regions. Besides, given the sparse video input, we enforce the model to reconstruct dense motion trajectories in both spatial and temporal dimensions. Pre-trained with our MME paradigm, the model is able to anticipate long-term and fine-grained motion details. Code is available at https://github.com/XinyuSun/MME.

cs.CV↗

arXiv2023

Laser Cooling of Nuclear Magnons

The initialization of nuclear spin to its ground state is challenging due to its small energy scale compared with thermal energy, even at cryogenic temperature. In this Letter, we propose an opto-nuclear quadrupolar effect, whereby two-color optical photons can efficiently interact with nuclear spins. Leveraging such an optical interface, we demonstrate that nuclear magnons, the collective excitations of nuclear spin ensemble, can be cooled down optically. Under feasible experimental conditions, laser cooling can suppress the population and entropy of nuclear magnons by more than two orders of magnitude, which could facilitate the application of nuclear spins in quantum information science.

quant-ph↗

arXiv2023

Two-photon Interface of Nuclear Spins Based on the Opto-Nuclear Quadrupolar Effect

Photons and nuclear spins are two well-known building blocks in quantum information science and technology. Establishing an efficient interface between optical photons and nuclear spins, while highly desirable for hybridizing these two quantum systems, is challenging because the interactions between nuclear spins and the environment are usually weak in magnitude, and there is also a formidable gap between nuclear spin frequencies and optical frequencies. In this work, we propose an opto-nuclear quadrupolar (ONQ) effect, whereby optical photons can be efficiently coupled to nuclear spins, similar to Raman scattering. Compared to previous works, ancilla electron spins are not required for the ONQ effect. This leads to advantages such as applicability in defect-free nonmagnetic crystals and longer nuclear spin coherence time. In addition, the frequency of the optical photons can be arbitrary, so they can be fine-tuned to minimize the material heating and to match telecom wavelengths for long-distance communications. Using perturbation theory and first-principles calculations, we demonstrate that the ONQ effect is stronger by several orders of magnitude than other nonlinear optical effects that could couple to nuclear spins. Based on this rationale, we propose promising applications of the ONQ effect, including quantum memory, quantum transduction, and materials isotope spectroscopy. We also discuss issues relevant to the experimental demonstration of the ONQ effect.

quant-ph↗

arXiv2023

Manipulating solid-state spin concentration through charge transport

Solid-state spin defects are attractive candidates for developing quantum sensors and simulators. The spin and charge degrees of freedom in large defect ensembles are a promising platform to explore complex many-body dynamics and the emergence of quantum hydrodynamics. However, many interesting properties can be revealed only upon changes in the density of defects, which instead is usually fixed in material systems. Increasing the interaction strength by creating denser defect ensembles also brings more decoherence. Ideally one would like to control the spin concentration at will, while keeping fixed decoherence effects. Here we show that by exploiting charge transport, we can take some first steps in this direction, while at the same time characterizing charge transport and its capture by defects. By exploiting the cycling process of ionization and recombination of NV centers in diamonds, we pump electrons from the valence band to the conduction band. These charges are then transported to modulate the spin concentration by changing the charge state of material defects. By developing a wide-field imaging setup integrated with a fast single photon detector array, we achieve a direct and efficient characterization of the charge redistribution process by measuring the complete spectrum of the spin bath with micrometer-scale spatial resolution. We demonstrate the concentration increase of the dominant spin defects by a factor of 2 while keeping the $T_2$ of the NV center, which also provides a potential experimental demonstration of the suppression of spin flip-flops via hyperfine interactions. Our work paves the way to studying many-body dynamics with temporally and spatially tunable interaction strengths in hybrid charge-spin systems.

quant-ph↗

arXiv2023

Fast wide-field quantum sensor based on solid-state spins integrated with a SPAD array

Achieving fast, sensitive, and parallel measurement of a large number of quantum particles is an essential task in building large-scale quantum platforms for different quantum information processing applications such as sensing, computation, simulation, and communication. Current quantum platforms in experimental atomic and optical physics based on CMOS sensors and CCD cameras are limited by either low sensitivity or slow operational speed. Here we integrate an array of single-photon avalanche diodes with solid-state spin defects in diamond to build a fast wide-field quantum sensor, achieving a frame rate up to 100~kHz. We present the design of the experimental setup to perform spatially resolved imaging of quantum systems. A few exemplary applications, including sensing DC and AC magnetic fields, temperature, strain, local spin density, and charge dynamics, are experimentally demonstrated using an NV ensemble diamond sample. The developed photon detection array is broadly applicable to other platforms such as atom arrays trapped in optical tweezers, optical lattices, donors in silicon, and rare earth ions in solids.

quant-ph↗

arXiv2023

Ion sensors with crown ether-functionalized nanodiamonds

Alkali metal ions such as sodium and potassium cations play fundamental roles in biology. Developing highly sensitive and selective methods to both detect and quantify these ions is of considerable importance for medical diagnostics and bioimaging. Fluorescent nanoparticles have emerged as powerful tools for nanoscale imaging, but their optical properties need to be supplemented with specificity to particular chemical and biological signals in order to provide further information about biological processes. Nitrogen-vacancy (NV) centers in diamond are particularly attractive as fluorescence markers, thanks to their optical stability, biocompatibility and further ability to serve as highly sensitive quantum sensors of temperature, magnetic and electric fields in ambient conditions. In this work, by covalently grafting crown ether structures on the surface of nanodiamonds (NDs), we build sensors that are capable of detecting specific alkali ions such as sodium cations. We will show that the presence of these metal ions modifies the charge state of NV centers inside the ND, which can then be read out by measuring their photoluminescence spectrum. Our work paves the way for designing selective biosensors based on NV centers in diamond.

quant-ph↗

arXiv2022

Digital noise spectroscopy with a quantum sensor

We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process using a single-qubit sensor under digital control modulation. This Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters that directly sample the power spectrum of the target noise in the sequency domain -- from which the auto-correlation function in the time domain, as well as the power spectrum in the frequency domain, can be reconstructed using linear transformations. Our method, which can also be seen as an implementation of frame-based noise spectroscopy, solves the fundamental difficulty in sampling continuous functions with digital filters by introducing a transformation that relates the arithmetic and logical time domains. In comparison to standard, frequency-based dynamical-decoupling noise spectroscopy protocols, the accuracy of our method is only limited by the sampling and discretization in the time space and can be easily improved, even under limited evolution time due to decoherence and hardware limitations. Finally, we experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond, discuss practical limitations of the method, and avenues for further improving the reconstruction accuracy.

quant-ph↗

arXiv2022

Mechano-lithography: stress anisotropy driven nematic order in growing three-dimensional bacterial biofilms

Living active collectives have evolved with remarkable self-patterning ability to meet the physical and biological constraints for growth and survival. However, how complex multicellular patterns emerge from a single founder cell remains elusive. Here, by recourse to an agent-based model, we track the three-dimensional (3D) morphodynamics and cell orientational order of growing bacterial biofilms encased by agarose gels. Confined growth causes spatiotemporally heterogeneous stress buildup in the biofilm. High hydrostatic and low shear stresses at the core of the biofilm promote viscous-to-elastic transition and randomize cell packing, whereas the opposite stress state near the gel-cell interface drives nematic ordering with a time delay inherent to shear stress relaxation. Overall, stress anisotropy spatiotemporally coincides with nematic order in the confined biofilms, suggesting an anisotropic-stress-driven ordering mechanism. The strong reciprocity between stress anisotropy and cell ordering inspires innovative 3D mechano-lithography of living active collectives for a variety of environmental and biomedical applications.

physics.bio-ph↗

arXiv2022

Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction

When conducting autonomous scanning for the online reconstruction of unknown indoor environments, robots have to be competent at exploring scene structure and reconstructing objects with high quality. Our key observation is that different tasks demand specialized scanning properties of robots: rapid moving speed and far vision for global exploration and slow moving speed and narrow vision for local object reconstruction, which are referred as two different scanning modes: explorer and reconstructor, respectively. When requiring multiple robots to collaborate for efficient exploration and fine-grained reconstruction, the questions on when to generate and how to assign those tasks should be carefully answered. Therefore, we propose a novel asynchronous collaborative autoscanning method with mode switching, which generates two kinds of scanning tasks with associated scanning modes, i.e., exploration task with explorer mode and reconstruction task with reconstructor mode, and assign them to the robots to execute in an asynchronous collaborative manner to highly boost the scanning efficiency and reconstruction quality. The task assignment is optimized by solving a modified Multi-Depot Multiple Traveling Salesman Problem (MDMTSP). Moreover, to further enhance the collaboration and increase the efficiency, we propose a task-flow model that actives the task generation and assignment process immediately when any of the robots finish all its tasks with no need to wait for all other robots to complete the tasks assigned in the previous iteration. Extensive experiments have been conducted to show the importance of each key component of our method and the superiority over previous methods in scanning efficiency and reconstruction quality.

cs.RO↗

arXiv2022

Biofilms as self-shaping growing nematics

Active nematics are the nonequilibrium analog of passive liquid crystals in which anisotropic units consume free energy to drive emergent behavior. Similar to liquid crystal (LC) molecules in displays, ordering and dynamics in active nematics are sensitive to boundary conditions; however, unlike passive liquid crystals, active nematics, such as those composed of living matter, have the potential to regulate their boundaries through self-generated stresses. Here, using bacterial biofilms confined by a hydrogel as a model system, we show how a three-dimensional, living nematic can actively shape itself and its boundary in order to regulate its internal architecture through growth-induced stresses. We show that biofilms exhibit a sharp transition in shape from domes to lenses upon changing environmental stiffness or cell-substrate friction, which is explained by a theoretical model considering the competition between confinement and interfacial forces. The growth mode defines the progression of the boundary, which in turn determines the trajectories and spatial distribution of cell lineages. We further demonstrate that the evolving boundary defines the orientational ordering of cells and the emergence of topological defects in the interior of the biofilm. Our findings reveal novel self-organization phenomena in confined active matter and provide strategies for guiding the development of programmed microbial consortia with emergent material properties.

q-bio.QM↗

arXiv2022

A Modeling of TSRCG and Resource Optimization for Multi-task Delivery Guarantee Algorithm Based on CGR Strategy in LEO Satellite Network

With the reduction of satellite costs and the enhancement of processing capabilities, low earth orbit (LEO) satellite constellations can independently build inter-satellite networks without relying on traditional ground stations restricted by geographical distribution and can establish inter-satellite links (ISLs) and complete computing and routing on-board. The characteristics of frequent on-off ISLs, the highly dynamic network topology of satellite networks make it face the challenges of routing strategy design as a delay/interruption tolerant network (DTN). As a deterministic dynamic routing algorithm, contact graph routing (CGR) uses a contact plan to calculate the path and forward data, but it still has problems such as high computational overhead, low prediction accuracy caused by ignoring queue delay, and overbooked problem caused by limited cache. Therefore, we first start with the time-space resource contact graph (TSRCG) to accurately characterize the time-varying and predictable characteristics of the satellite network and the network resource parameters under multi-tasks. Then, we optimize the route-list computation and dynamic route computation process to ensure task delivery and reduce the consumption of various resources, such as contact capacity, computing resources, and storage resources. And the resource optimization for the multi-task delivery guarantee algorithm based on CGR (RMDG-CGR) strategy we propose is compared with standard CGR in ION 4.0.1. Finally, the simulation results show that the RMDG-CGR can achieve higher task delivery in advance and successful task delivery rate, save contact volume occupancy rate, computing and storage resource, and the above effects are more prominent, especially in the task scenario with critical bundles.

cs.NI↗

arXiv2022

Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation

In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the case of excessive network dynamics. DTN node buffer management is still an active area of research, as the current implementation of the DTN core protocol still relies on the assumption that there is always enough memory available in different network nodes to store and forward bundles. In addition, the classical queuing theory does not apply to the dynamic management of DTN node buffers. Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C). The method aims to explore training a geosynchronous earth orbit intelligent agent to manage all DTN nodes in an LEO satellite constellation scenario. The goal of the A2C agent is to maximize delivery success rate and minimize network resource consumption cost while considering node memory utilization. The intelligent agent can dynamically adjust the radio data rate and perform drop operations based on bundle priority. In order to measure the effectiveness of applying A2C technology to DTN node management issues in LEO satellite constellation scenarios, this paper compares the trained intelligent agent strategy with the other two non-RL policies, including random and standard policies. Experiments show that the A2C strategy balances delivery success rate and cost, and provides the highest reward and the lowest node memory utilization.

cs.AI↗

arXiv2022

Communication-efficient Quantum Algorithm for Distributed Machine Learning

The growing demands of remote detection and increasing amount of training data make distributed machine learning under communication constraints a critical issue. This work provides a communication-efficient quantum algorithm that tackles two traditional machine learning problems, the least-square fitting and softmax regression problem, in the scenario where the data set is distributed across two parties. Our quantum algorithm finds the model parameters with a communication complexity of $O(\frac{\log_2(N)}ε)$, where $N$ is the number of data points and $ε$ is the bound on parameter errors. Compared to classical algorithms and other quantum algorithms that achieve the same output task, our algorithm provides a communication advantage in the scaling with the data volume. The building block of our algorithm, the quantum-accelerated estimation of distributed inner product and Hamming distance, could be further applied to various tasks in distributed machine learning to accelerate communication.

quant-ph↗

arXiv2022

Characterizing temperature and strain variations with qubit ensembles for their robust coherence protection

Solid-state spin defects, especially nuclear spins with potentially achievable long coherence times, are compelling candidates for quantum memories and sensors. However, their current performances are still limited by the decoherence due to the variation of their intrinsic quadrupole and hyperfine interactions. We propose an \textit{unbalanced echo} to overcome this challenge by using a second spin to refocus the variation of these interactions, which preserves the quantum information stored in the free evolution. The unbalanced echo can be used to probe the temperature and strain distribution in materials. Experimentally, we demonstrate a 20-fold $T_2^*$ coherence time increase in an ensemble of $\sim10^{10}$ nuclear spins in diamond. Theoretically, we develop first-principles methods to predict these interaction variations and reveal their correlation in large temperature and strain ranges. We numerically show that our method can refocus stronger noise variations than our current experiments and achieves a 400-fold coherence improvement for a 25~K temperature inhomogeneity.

quant-ph↗

arXiv2021

SARS-CoV-2 quantum sensor based on nitrogen-vacancy centers in diamond

The development of highly sensitive and rapid biosensing tools targeted to the highly contagious virus SARS-CoV-2 is critical to tackling the COVID-19 pandemic. Quantum sensors can play an important role, thanks to their superior sensitivity and fast improvements in recent years. Here we propose a molecular transducer designed for nitrogen-vacancy (NV) centers in nanodiamonds, translating the presence of SARS-CoV-2 RNA into an unambiguous magnetic noise signal that can be optically read out. We evaluate the performance of the hybrid sensor, including its sensitivity and false negative rate, and compare it to widespread diagnostic methods. The proposed method is fast and promises to reach a sensitivity down to a few hundreds of RNA copies with false negative rate less than 1%. The proposed hybrid sensor can be further implemented with different solid-state defects and substrates, generalized to diagnose other RNA viruses, and integrated with CRISPR technology.

quant-ph↗

arXiv2021

Biofilm self-patterning: mechanical forces drive a reorientation cascade

In growing active matter systems, a large collection of engineered or living autonomous units metabolize free energy and create order at different length scales as they proliferate and migrate collectively. One such example is bacterial biofilms, which are surface-attached aggregates of bacterial cells embedded in an extracellular matrix. However, how bacterial growth coordinates with cell-surface interactions to create distinctive, long-range order in biofilms remains elusive. Here we report a collective cell reorientation cascade in growing Vibrio cholerae biofilms, leading to a differentially ordered, spatiotemporally coupled core-rim structure reminiscent of a blooming aster. Cell verticalization in the core generates differential growth that drives radial alignment of the cells in the rim, while the radially aligned rim in turn generates compressive stresses that expand the verticalized core. Such self-patterning disappears in adhesion-less mutants but can be restored through opto-manipulation of growth. Agent-based simulations and two-phase active nematic modeling reveal the strong interdependence of the driving forces for the differential ordering. Our findings provide insight into the collective cell patterning in bacterial communities and engineering of phenotypes and functions of living active matter.

cond-mat.soft↗

arXiv2021

Observation of symmetry-protected selection rules in periodically driven quantum systems

Periodically driven quantum systems, known as Floquet systems, have been a focus of non-equilibrium physics in recent years, thanks to their rich dynamics. Not only time-periodic systems exhibit symmetries similar to those in spatially periodic systems, but they also display novel behavior due to symmetry breaking. Characterizing such dynamical symmetries is crucial, but the task is often challenging, due to limited driving strength and the lack of an experimentally accessible characterization protocol. Here, we show how to characterize dynamical symmetries including parity, rotation, and particle-hole symmetry by observing the symmetry-induced selection rules between Floquet states. Specifically, we exploit modulated quantum driving to reach the strong light-matter coupling regime and we introduce a protocol to experimentally extract the transition elements between Floquet states from the coherent evolution of the system. Using the nitrogen-vacancy center in diamond as an experimental testbed, we apply our methods to observe symmetry-protected dark states and dark bands, and the coherent destruction of tunneling effect. Our work shows how to exploit the quantum control toolkit to study dynamical symmetries that can arise in topological phases of strongly-driven Floquet systems.

quant-ph↗