arXiv subjects

Mengqi Wang

Publications and source records attributed to Mengqi Wang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Entanglement-Enhanced Nanoscale Single-Spin Sensing

Detecting individual spins--including stable and metastable states--represents a fundamental challenge in quantum sensing with broad applications across condensed matter physics, quantum chemistry, and single-molecule magnetic resonance imaging. While nitrogen-vacancy (NV) centers in diamond have emerged as powerful nanoscale sensors, their performance for single-spin detection remains constrained by substantial environmental noise and restricted sensing volume. Here, we propose and demonstrate an entanglement-enhanced sensing protocol that overcomes these limitations through the strategic use of entangled NV pairs. Our approach achieves a 3.4-fold enhancement in sensitivity and a 1.6-fold reduction in spatial resolution relative to single NV centers under ambient conditions. The protocol employs carefully engineered entangled states that amplify target spin signals through quantum interference while suppressing environmental noise. Crucially, we extend these capabilities to resolve metastable single-spin dynamics, directly observing stochastic transitions between different spin states by identifying state-dependent coupling strengths. This dual functionality enables simultaneous detection of static and dynamic spin species for studying complex quantum systems. The achieved performance establishes entanglement-enhanced sensing as a viable pathway toward atomic-scale characterization of quantum materials and interface.

quant-ph↗

arXiv2025

Fundamentals of non-parametric statistical inference for integrated quantiles

We present a general non-parametric statistical inference theory for integrals of quantiles without assuming any specific sampling design or dependence structure. Technical considerations are accompanied by examples and discussions, including those pertaining to the bias of empirical estimators. To illustrate how the general results can be adapted to specific situations, we derive - at a stroke and under minimal conditions - consistency and asymptotic normality of the empirical tail-value-at-risk, Lorenz and Gini curves at any probability level in the case of the simple random sampling, thus facilitating a comparison of our results with what is already known in the literature. Results, notes and references concerning dependent (i.e., time series) data are also offered. As a by-product, our general results provide new and unified proofs of large-sample properties of a number of classical statistical estimators, such as trimmed means, and give additional insights into the origins of, and the reasons for, various necessary and sufficient conditions.

math.ST↗

arXiv2024

Machine Learning Aided Modeling of Granular Materials: A Review

Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

physics.geo-ph↗

arXiv2024

3D scene generation from scene graphs and self-attention

Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality. As concise and robust representations of a scene, scene graphs have proven to be well-suited as the semantic control on the generated layout. We present a variant of the conditional variational autoencoder (cVAE) model to synthesize 3D scenes from scene graphs and floor plans. We exploit the properties of self-attention layers to capture high-level relationships between objects in a scene, and use these as the building blocks of our model. Our model, leverages graph transformers to estimate the size, dimension and orientation of the objects in a room while satisfying relationships in the given scene graph. Our experiments shows self-attention layers leads to sparser (7.9x compared to Graphto3D) and more diverse scenes (16%).

cs.CV↗

arXiv2024

Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.

quant-ph↗

arXiv2024

Correlated sensing with a solid-state quantum multi-sensor system for atomic-scale structural analysis

Developing superior quantum sensing strategies ranging from ultra-high precision measurement to complex structural analysis is at the heart of quantum technologies. While strategies using quantum resources, such as entanglement among sensors, to enhance the sensing precision have been abundantly demonstrated, the signal correlation among quantum sensors is rarely exploited. Here we develop a novel sensing paradigm exploiting the signal correlation among multiple quantum sensors to resolve overlapping signals from multiple targets that individual sensors can't resolve and complex structural construction struggles with. With three nitrogen-vacancy centers as a quantum electrometer system, we demonstrate this multi-sensor paradigm by resolving individual defects' fluctuating electric fields from ensemble signals. We image the three-dimensional distribution of 16 dark electronic point-defects in diamond with accuracy approaching 1.7 nm via a GPS-like localization method. Furthermore, we obtain the real-time charge dynamics of individual point defects and visualize how the dynamics induce the well-known optical spectral diffusion. The multi-sensor paradigm extends the quantum sensing toolbox and offers new possibilities for structural analysis.

quant-ph↗

arXiv2023

Imaging magnetism evolution of magnetite to megabar pressure range with quantum sensors in diamond anvil cell

High-pressure diamond anvil cells have been widely used to create novel states of matter. Nevertheless, the lack of universal in-situ magnetic measurement techniques at megabar pressures makes it difficult to understand the underlying physics of materials' behavior at extreme conditions, such as high-temperature superconductivity of hydrides and the formation or destruction of the local magnetic moments in magnetic systems, etc. Here we break through the limitations of pressure on quantum sensors and develop the in-situ magnetic detection technique at megabar pressures with high sensitivity (~1μT/Hz^(1\2)) and sub-microscale spatial resolution. By directly imaging the magnetic field and the evolution of magnetic domains, we observe the macroscopic magnetic transition of Fe3O4 in the megabar pressure range from strong ferromagnetism (α-Fe3O4) to weak ferromagnetism (β-Fe3O4) and finally to non-magnetism (γ-Fe3O4). The scenarios for magnetic changes in Fe3O4 characterized here shed light on the direct magnetic microstructure observation in bulk materials at high pressure and contribute to understanding the mechanism of magnetic moment suppression related to spin crossover. The presented method can potentially investigate the spin-orbital coupling and magnetism-superconductivity competition in magnetic systems.

physics.app-ph↗

arXiv2022

99.92%-Fidelity CNOT Gates in Solids by Filtering Time-dependent and Quantum Noises

Inevitable interactions with the reservoir largely degrade the performance of non-local gates, which hinders practical quantum computation from coming into existence. Here we experimentally demonstrate a 99.920(7)\%-fidelity controlled-NOT gate by suppressing the complicated noise in a solid-state spin system at room temperature. We found that the fidelity limited at 99\% in previous works results from only considering static noise, and thus, in this work, time-dependent noise and quantum noise are also included. All noises are dynamically corrected by an exquisitely designed shaped pulse, giving the resulting error below $10^{-4}$. The residual gate error is mainly originated from the longitudinal relaxation and the waveform distortion that can both be further reduced technically. Our noise-resistant method is universal, and will benefit other solid-state spin systems.

quant-ph↗

arXiv2022

Observation of uniaxial strain tuned spin cycloid in a freestanding BiFeO3 film

Non-collinear spin order that breaks space inversion symmetry and allows efficient electric-field control of magnetism makes BiFeO$_3$ a promising candidate for applications in low-power spintronic devices. Epitaxial strain effects have been intensively studied and exhibit significant modulation of the magnetic order in BiFeO$_3$, but tuning its spin structure with continuously varied uniaxial strain is still lacking up to date. Here, we apply \emph{in situ} uniaxial strain to a freestanding BiFeO$_3$ film and use scanning NV microscope to image the nanoscale magnetic order in real-space. The strain is continuously increased from 0\% to 1.5\% and four images under different strains are acquired during this period. The images show that the spin cycloid tilts by $\sim 12.6^\circ$ when strain approaches 1.5\%. A first principle calculation has been processed to show that the tilting is energetically favorable under such strain. Our \emph{in situ} strain applying method in combination with scanning NV microscope real-space imaging ability paves a new way in studying the coupling between magnetic order and strain in BiFeO$_3$ films.

cond-mat.mtrl-sci↗

arXiv2022

Self-aligned patterning technique for fabricating high-performance diamond sensor arrays with nanoscale precision

To efficiently align the creation of defect center with photonics structure in nanoscale precision is one of the outstanding challenges for realizing high-performance photonic devices and the application in quantum technology such as quantum sensing, scalable quantum systems, and quantum computing network. Here, we propose a facile self-aligned patterning technique wholly based on conventional engineering technology, with the doping precision can reach ~15nm. Specifically, we demonstrate this technique by fabricating diamond nanopillar sensor arrays, which show high consistency and near-optimal photon counts, high yield approaching the theoretical limit, and high filtering efficiency for different NV centers. Combined with appropriate crystal orientation, a saturated fluorescence rate of 4.65 Mcps and the best reported fluorescence-dependent detection sensitivity of 1900 cps^(-1/2) are achieved. This technique applicable to all similar solid-state systems should facilitate the development of parallel quantum sensing and scalable information processing.

physics.app-ph↗

arXiv2022

Spin Quantum Heat Engine Quantified by Quantum Steering

Following the rising interest in quantum information science, the extension of a heat engine to the quantum regime by exploring microscopic quantum systems has seen a boom of interest in the last decade. Although quantum coherence in the quantum system of the working medium has been investigated to play a nontrivial role, a complete understanding of the intrinsic quantum advantage of quantum heat engines remains elusive. We experimentally demonstrate that the quantum correlation between the working medium and the thermal bath is critical for the quantum advantage of a quantum Szilard engine, where quantum coherence in the working medium is naturally excluded. By quantifying the non-classical correlation through quantum steering, we reveal that the heat engine is quantum when the demon can truly steer the working medium. The average work obtained by taking different ways of work extraction on the working medium can be used to verify the real quantum Szilard engine.

quant-ph↗

arXiv2022

Resonant Quantum Principal Component Analysis

Principal component analysis has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the principal components of it, i.e. the eigenvectors of the density matrix with largest eigenvalues. However, due to the substantial resource requirement, its experimental implementation remains challenging. Here, we develop a resonant analysis algorithm with the minimal resource for ancillary qubits, in which only one frequency scanning probe qubit is required to extract the principal components. In the experiment, we demonstrate the distillation of the first principal component of a 4$\times$4 density matrix, with the efficiency of 86.0% and fidelity of 0.90. This work shows the speed-up ability of quantum algorithm in dimension reduction of data and thus could be used as part of quantum artificial intelligence algorithms in the future.

quant-ph↗

arXiv2021

Excited-state spectroscopy of spin defects in hexagonal boron nitride

We used optically detected magnetic resonance (ODMR) technique to directly probe electron-spin resonance transitions in the excited state of negatively-charged boron vacancy (VB-) defects in hexagonal boron nitride (hBN) at room temperature. The data showed that the excited state has a zero-field splitting of ~ 2.1 GHz, a g factor similar to the ground state and two types of hyperfine splitting ~ 90 MHz and ~ 18.8 MHz respectively. Pulsed ODMR experiments were conducted to further verify observed resonant peaks corresponding to spin transitions in the excited state. In addition, negative peaks in photoluminescence and ODMR contrast as a function of magnetic field magnitude and angle at level anti-crossing were observed and explained by coherent spin precession and anisotropic relaxation. This work provided significant insights for studying the structure of VB- excited states, which might be used for quantum information processing and nanoscale quantum sensing.

quant-ph↗

arXiv2021

Federated Learning Meets Natural Language Processing: A Survey

Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models. However, both big deep neural and language models are trained with huge amounts of data which often lies on the server side. Since text data is widely originated from end users, in this work, we look into recent NLP models and techniques which use federated learning as the learning framework. Our survey discusses major challenges in federated natural language processing, including the algorithm challenges, system challenges as well as the privacy issues. We also provide a critical review of the existing Federated NLP evaluation methods and tools. Finally, we highlight the current research gaps and future directions.

cs.CL↗

arXiv2021

Identity Test of Single NV$^-$ Centers in Diamond at Hz-Precision Level

Atomic-like defects in solids are not considered to be identical owing to the imperfections of host lattice. Here, we found that even under ambient conditions, negatively charged nitrogen-vacancy (NV$^-$) centers in diamond could still manifest identical at Hz-precision level, corresponding to a 10$^{-7}$-level relative precision, while the lattice strain can destroy the identity by tens of Hz. All parameters involved in the NV$^-$-$^{14}$N Hamiltonian are determined by formulating six nuclear frequencies at 10-mHz-level precision and measuring them at Hz-level precision. The most precisely measured parameter, the $^{14}$N quadrupole coupling $P$, is given by -4945754.9(8) Hz, whose precision is improved by nearly four orders of magnitude compared with previous measurements. We offer an approach for performing precision measurements in solids and deepening our understandings of NV centers as well as other solid-state defects. Besides, these high-precision results imply a potential application of a robust and integrated atomic-like clock based on ensemble NV centers.

quant-ph↗

arXiv2021

Beating the Standard Quantum Limit under Ambient Conditions with Solid-State Spins

Precision measurement plays a crucial role in all fields of science. The use of entangled sensors in quantum metrology improves the precision limit from the standard quantum limit (SQL) to the Heisenberg limit (HL). To date, most experiments beating the SQL are performed on the sensors which are well isolated under extreme conditions. However, it has not been realized in solid-state spin systems at ambient conditions, owing to its intrinsic complexity for the preparation and survival of pure and entangled quantum states. Here we show a full interferometer sequence beating the SQL by employing a hybrid multi-spin system, namely the nitrogen-vacancy (NV) defect in diamond. The interferometer sequence starts from a deterministic and joint initialization, undergoes entanglement and disentanglement of multiple spins, and ends up with projective measurement. In particular, the deterministic and joint initialization of NV negative state, NV electron spin, and two nuclear spins is realized at room temperature for the first time. By means of optimal control, non-local gates are implemented with an estimated fidelity above the threshold for fault-tolerant quantum computation. With these techniques combined, we achieve two-spin interference with a phase sensitivity of 1.79 \pm 0.06 dB beyond the SQL and three-spin 2.77 \pm 0.10 dB. Moreover, the deviations from the HL induced by experimental imperfections are completely accountable. The techniques used here are of fundamental importance for quantum sensing and computing, and naturally applicable to other solid-state spin systems.

quant-ph↗

arXiv2020

Parallel optically detected magnetic resonance spectrometer for dozens of single nitrogen-vacancy centers using laser-spot lattice

We develop a parallel optically detected magnetic resonance (PODMR) spectrometer to address, manipulate and read out an array of single nitrogen-vacancy (NV) centers in diamond in parallel. In this spectrometer, we use an array of micro-lens to generate 20 * 20 laser-spot lattice (LSL) on the objective focal plane, and then align the LSL with an array of single NV centers. The quantum states of NV centers are manipulated by a uniform microwave field from a Ω-shape coplanar coil. As an experimental demonstration, we observe 80 NV centers in the field of view. Among them, magnetic resonance (MR) spectrums and Rabi oscillations of 18 NV centers along the external magnetic field are measured in parallel. These results can be directly used to realize parallel quantum sensing and multiple times speedup compared with the confocal technique. Regarding the nanoscale MR technique, PODMR will be crucial for high throughput single molecular MR spectrum and imaging.

physics.app-ph↗

arXiv2020

High-fidelity single-shot readout of single electron spin in diamond with spin-to-charge conversion

High fidelity single-shot readout of qubits is a crucial component for fault-tolerant quantum computing and scalable quantum networks. In recent years, the nitrogen-vacancy (NV) center in diamond has risen as a leading platform for the above applications. The current single-shot readout of the NV electron spin relies on resonance fluorescence method at cryogenic temperature. However, the the spin-flip process interrupts the optical cycling transition, therefore, limits the readout fidelity. Here, we introduce a spin-to-charge conversion method assisted by near-infrared (NIR) light to suppress the spin-flip error. This method leverages high spin-selectivity of cryogenic resonance excitation and high flexibility of photonionization. We achieve an overall fidelity $>$ 95% for the single-shot readout of an NV center electron spin in the presence of high strain and fast spin-flip process. With further improvements, this technique has the potential to achieve spin readout fidelity exceeding the fault-tolerant threshold, and may also find applications on integrated optoelectronic devices.

quant-ph↗