arXiv subjects

Jing Qian

Publications and source records attributed to Jing Qian.

At least 37 records · Page 2Linked to original sources

arXiv2025

Multiobjective Optimization for Robust Holonomic Quantum Gates

The practical implementation of high-fidelity quantum gates faces significant challenges in simultaneously mitigating multiple operational errors arising from distinct physical mechanisms. These errors often span orders of magnitude in severity, and their respective suppression strategies may inherently conflict. In this work, we develop a universal multiobjective optimization framework for quantum gate design by integrating Pareto optimal solutions with an entropy-weight method. Using Rydberg-based nonadiabatic holonomic quantum gates (affected by amplitude errors, detuning errors, and Rydberg decoherence) as a testbed, we theoretically demonstrate the superiority of our algorithm. The optimized gates exhibit enhanced fidelity and robustness compared to those derived from one-objective optimization strategies. Furthermore, this framework is readily adaptable to other quantum gate protocols and provides a robust foundation for advancing fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Cam-2-Cam: Exploring the Design Space of Dual-Camera Interactions for Smartphone-based Augmented Reality

Off-the-shelf smartphone-based AR systems typically use a single front-facing or rear-facing camera, which restricts user interactions to a narrow field of view and small screen size, thus reducing their practicality. We present Cam-2-Cam, an interaction concept implemented in three smartphone-based AR applications with interactions that span both cameras. Results from our qualitative analysis conducted on 30 participants presented two major design lessons that explore the interaction space of smartphone AR while maintaining critical AR interface attributes like embodiment and immersion: (1) Balancing Contextual Relevance and Feedback Quality serves to outline a delicate balance between implementing familiar interactions people do in the real world and the quality of multimodal AR responses and (2) Preventing Disorientation using Simultaneous Capture and Alternating Cameras which details how to prevent disorientation during AR interactions using the two distinct camera techniques we implemented in the paper. Additionally, we consider observed user assumptions or natural tendencies to inform future implementations of dual-camera setups for smartphone-based AR. We envision our design lessons as an initial pioneering step toward expanding the interaction space of smartphone-based AR, potentially driving broader adoption and overcoming limitations of single-camera AR.

cs.HC↗

arXiv2025

Mitigating the phase-mismatch effect in non-resonant four-wave mixing enabled by optimal control

Phase-mismatch in nonlinear optical processes can severely limit the propagation and conversion efficiency of light fields. Here, we present an efficient optimal-control strategy to mitigate the detrimental effects of phase-mismatch in an electromagnetically induced transparency medium via non-resonant four-wave mixing (FWM). By applying a set of fixed, linearly modulated coupling fields that induce a dark eigenmode, we globally optimize a single coupling detuning to minimize the spontaneous emission loss, the primary factor limiting conversion efficiency. Our approach outperforms existing FWM schemes by providing strong robustness against large phase-mismatch variations while maintaining efficient probe-to-signal conversion. These results offer a promising route toward more efficient nonlinear frequency conversion, alleviating the stringent requirement for phase matching in experiments.

quant-ph↗

arXiv2025

Design and Implementation of the Transparent, Interpretable, and Multimodal (TIM) AR Personal Assistant

The concept of an AI assistant for task guidance is rapidly shifting from a science fiction staple to an impending reality. Such a system is inherently complex, requiring models for perceptual grounding, attention, and reasoning, an intuitive interface that adapts to the performer's needs, and the orchestration of data streams from many sensors. Moreover, all data acquired by the system must be readily available for post-hoc analysis to enable developers to understand performer behavior and quickly detect failures. We introduce TIM, the first end-to-end AI-enabled task guidance system in augmented reality which is capable of detecting both the user and scene as well as providing adaptable, just-in-time feedback. We discuss the system challenges and propose design solutions. We also demonstrate how TIM adapts to domain applications with varying needs, highlighting how the system components can be customized for each scenario.

cs.ET↗

arXiv2025

Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling

Augmented Reality (AR) assistance is increasingly used for supporting users with physical tasks like assembly and cooking. However, most systems rely on reactive responses triggered by user input, overlooking rich contextual and user-specific information. To address this, we present Satori, a novel AR system that proactively guides users by modeling both -- their mental states and environmental contexts. Satori integrates the Belief-Desire-Intention (BDI) framework with the state-of-the-art multi-modal large language model (LLM) to deliver contextually appropriate guidance. Our system is designed based on two formative studies involving twelve experts. We evaluated the system with a sixteen within-subject study and found that Satori matches the performance of designer-created Wizard-of-Oz (WoZ) systems, without manual configurations or heuristics, thereby improving generalizability, reusability, and expanding the potential of AR assistance.

cs.HC↗

arXiv2025

My Boss is a Narcissist Bully: A Game Theoretic Approach to Stop Bullies

This paper investigates effective strategies for dealing with workplace bullying perpetrated by a narcissistic boss. Adopting a game-theoretic framework, we propose a three-stage sequential game with a simultaneous form game, incorporating a war of attrition in the final stage. Our findings demonstrate that victims of bullying should consistently choose to signal to escalate the situation and report bulling rather than ignore and tolerate the abusive behavior. Additionally, we explore how leveraging the narcissist's inherent fears can empower the victim in selecting the most advantageous equilibrium solution. By employing this comprehensive approach, individuals facing a narcissistic boss bullying can effectively address and mitigate these challenging circumstances.

econ.TH↗

arXiv2025

Towards An Efficient LLM Training Paradigm for CTR Prediction

Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significantly outperform conventional click-through-rate (CTR) prediction approaches. Despite such promising results, the computational inefficiency inherent in the current training paradigm makes it particularly challenging to train LLMs for ranking-based recommendation tasks on large datasets. To train LLMs for CTR prediction, most existing studies adopt the prevalent ''sliding-window'' paradigm. Given a sequence of $m$ user interactions, a unique training prompt is constructed for each interaction by designating it as the prediction target along with its preceding $n$ interactions serving as context. In turn, the sliding-window paradigm results in an overall complexity of $O(mn^2)$ that scales linearly with the length of user interactions. Consequently, a direct adoption to train LLMs with such strategy can result in prohibitively high training costs as the length of interactions grows. To alleviate the computational inefficiency, we propose a novel training paradigm, namely Dynamic Target Isolation (DTI), that structurally parallelizes the training of $k$ (where $k >> 1$) target interactions. Furthermore, we identify two major bottlenecks - hidden-state leakage and positional bias overfitting - that limit DTI to only scale up to a small value of $k$ (e.g., 5) then propose a computationally light solution to effectively tackle each. Through extensive experiments on three widely adopted public CTR datasets, we empirically show that DTI reduces training time by an average of $\textbf{92%}$ (e.g., from $70.5$ hrs to $5.31$ hrs), without compromising CTR prediction performance.

cs.IR↗

arXiv2025

Quantum-preserved transport of excitations in Rydberg-dressed atom arrays

To transport high-quality quantum state between two distant qubits through one-dimensional spin chains, the perfect state transfer (PST) method serves as the first choice, due to its natively perfect transfer fidelity that is independent of the system dimension. However, the PST requires a precise modulation of the local pulse parameters as well as an accurate timing of dynamic evolution, and is thus very sensitive to variations in practice. Here, we propose a protocol for achieving quantum-preserved transport of excitations using an array of Rydberg-dressed atoms, enabled by optimal control of minimally global parameters. By treating the weak coupling of two marginal array atoms as a perturbation, an effective spin-exchange model with highly tunable interactions between the external weak and the inner strong driving atoms can be established, which allows for coherent excitation transfer even with large atomic position fluctuation. We furthermore show that the existence of long-time excitation propagation unattainable for systems under antiblockade facilitation conditions. Our results highlight an easily-implemented scheme for studying the dynamics of spin systems using Rydberg atoms and may guide the avenue to the engineering of complex many-body dynamics.

quant-ph↗

arXiv2025

AdaptiveCoPilot: Design and Testing of a NeuroAdaptive LLM Cockpit Guidance System in both Novice and Expert Pilots

Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot's cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware Large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

cs.HC↗

arXiv2024

Active robustness against the detuning-error for Rydberg quantum gates

Error suppression to the experimental imperfections is a central challenge for useful quantum computing. Recent studies have shown the advantages of using single-modulated pulses based on optimal control which can realize high-fidelity two-qubit gates in neutral-atom arrays. However, typical optimization only minimizes the ideal gate error in the absence of any decay, which allows the gate to be passively influenced by all error sources leading to an exponential increase of sensitivity when the error becomes larger. In the present work, we propose the realization of two-qubit CZ gates with active robustness against two-photon detuning errors. Our method depends on a modified cost function in numerical optimization for shaping gate pulses, which can minimize, not only the ideal gate error but also the fluctuations of gate infidelity over a wide error range. We introduce a family of Rydberg blockade gates with active robustness towards the impacts of versatile noise sources such as Doppler dephasing and ac Stark shifts. The resulting gates with robust pulses can significantly increase the insensitivity to any type of errors acting on the two-photon detuning, benefiting from a relaxed requirement of colder atomic temperatures or more stable lasers for current experimental technology.

quant-ph↗

arXiv2024

Interference-Cancellation-Based Channel Knowledge Map Construction and Its Applications to Channel Estimation

Channel knowledge map (CKM) is viewed as a digital twin of wireless channels, providing location-specific channel knowledge for environment-aware communications. A fundamental problem in CKM-assisted communications is how to construct the CKM efficiently. Current research focuses on interpolating or predicting channel knowledge based on error-free channel knowledge from measured regions, ignoring the extraction of channel knowledge. This paper addresses this gap by unifying the extraction and representation of channel knowledge. We propose a novel CKM construction framework that leverages the received signals of the base station (BS) as online and low-cost data. Specifically, we partition the BS coverage area into spatial grids. The channel knowledge per grid is represented by a set of multi-path powers, delays, and angles, based on the principle of spatial consistency. In the extraction of these channel parameters, the challenges lie in strong inter-cell interferences and non-linear relationship between received signals and channel parameters. To address these issues, we formulate the problem of CKM construction into a problem of Bayesian inference, employing a block-sparsity prior model to characterize the path-loss differences of interferers. Under the Bayesian inference framework, we develop a hybrid message-passing algorithm for the interference-cancellation-based CKM construction. Based on the CKM, we obtain the joint frequency-space covariance of user channel and design a CKM-assisted Bayesian channel estimator. The computational complexity of the channel estimator is substantially reduced by exploiting the CKM-derived covariance structure. Numerical results show that the proposed CKM provides accurate channel parameters at low signal-to-interference-plus-noise ratio (SINR) and that the CKM-assisted channel estimator significantly outperforms state-of-the-art counterparts.

cs.IT↗

arXiv2024

Erasing Doppler Dephasing Error in Rydberg Quantum Gates

The Doppler dephasing error due to residual thermal motion of qubit atoms is a major cause of fidelity loss in neutral-atom quantum gates. Besides cooling and trapping advancements, few effective methods exist to mitigate this error. In the present work, we introduce an error-erasing strategy that utilizes a pair of off-resonant fields to continuously dress the protected Rydberg state with an auxiliary state, which induces an opposite but enhanced sensitivity to the same source of Doppler dephasing error. Combining with an optimal control of laser pulses, we realize a family of Rydberg two-qubit controlled-NOT gates in Rb and Cs atoms that are fully robust to the Doppler dephasing error. We benchmark this gate operation with fidelity $F\approx0.9906$ at ${\it any}$ temperature for a lower-excited auxiliary state, and a higher fidelity of $F\approx0.9965$ can be attained for a ground-state auxiliary state at a temperature of 50 $μ$K. Our results significantly reduce atomic temperature requirements for high-fidelity quantum gates, and may provide fundamental guidance to practical error-tolerant quantum computing with neutral atoms.

quant-ph↗

arXiv2024

ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality

Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality.

cs.HC↗

arXiv2024

Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design. However, problem remains on how to unify the CE and MIMODet by optimizing AI's structure to achieve near optimal detection performance such as widely considered QR with M-algorithm (QRM) that can perform close to the maximum likelihood (ML) detector. In this paper, we propose an AI receiver that connects CE and MIMODet as an unified architecture. As a merit, CE and MIMODet only adopt structural input features and conventional neural networks (NN) to perform end-to-end (E2E) training offline. Numerical results show that, by adopting a simple super-resolution based convolutional neural network (SRCNN) as channel estimator and domain knowledge enhanced graphical neural network (GNN) as detector, the proposed QRM enhanced GNN receiver (QRMNet) achieves comparable block error rate (BLER) performance to near-optimal baseline detectors.

eess.SP↗

arXiv2023

High-tolerance antiblockade SWAP gates using optimal pulse drivings

Position error is treated as the leading obstacle that prevents Rydberg antiblockade gates from being experimentally realizable, because of the inevitable fluctuations in the relative motion between two atoms invalidating the antiblockade condition. In this work we report progress towards a high-tolerance antiblockade-based Rydberg SWAP gate enabled by the use of {\it modified} antiblockade condition combined with carefully-optimized laser pulses. Depending on the optimization of diverse pulse shapes our protocol shows that the amount of time-spent in the double Rydberg state can be shortened by more than $70\%$ with respect to the case using {\it perfect} antiblockade condition, which significantly reduces this position error. Moreover, we benchmark the robustness of the gate via taking account of the technical noises, such as the Doppler dephasing due to atomic thermal motion, the fluctuations in laser intensity and laser phase and the intensity inhomogeneity. As compared to other existing antiblockade-gate schemes the predicted gate fidelity is able to maintain at above 0.91 after a very conservative estimation of various experimental imperfections, especially considered for realistic interaction deviation of $δV/V\approx 5.92\%$ at $T\sim20$ $μ$K. Our work paves the way to the experimental demonstration of Rydberg antiblockade gates in the near future.

quant-ph↗

arXiv2023

ARGUS: Visualization of AI-Assisted Task Guidance in AR

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine tune the components of the AR assistant.

cs.HC↗

arXiv2023

Minimum Eigenvalue Based Covariance Matrix Estimation with Limited Samples

In this paper, we consider the interference rejection combining (IRC) receiver, which improves the cell-edge user throughput via suppressing inter-cell interference and requires estimating the covariance matrix including the inter-cell interference with high accuracy. In order to solve the problem of sample covariance matrix estimation with limited samples, a regularization parameter optimization based on the minimum eigenvalue criterion is developed. It is different from traditional methods that aim at minimizing the mean squared error, but goes straight at the objective of optimizing the final performance of the IRC receiver. A lower bound of the minimum eigenvalue that is easier to calculate is also derived. Simulation results demonstrate that the proposed approach is effective and can approach the performance of the oracle estimator in terms of the mutual information metric.

cs.IT↗

arXiv2023

Proposal for practical Rydberg quantum gates using a native two-photon excitation

Rydberg quantum gate serving as an indispensable computing unit for neutral-atom quantum computation, has attracted intense research efforts for the last decade. However the state-of-the-art experiments have not reached the high gate fidelity as predicted by most theories due to the unexpected large loss remaining in Rydberg and intermediate states. In this paper we report our findings in constructing a native two-qubit controlled-NOT gate based on pulse optimization. We focus on the method of commonly-used two-photon Rydberg excitation with smooth Gaussian-shaped pulses which is straightforward for experimental demonstration. By utilizing optimized pulse shapes the scheme reveals a remarkable reduction in the decays from Rydberg and intermediate states, as well as a high-tolerance to the residual thermal motion of atoms. We extract a conservative lower bound for the gate fidelity > 0.992 after taking into account the experimental imperfections. Our results not only reduce the gap between experimental and theoretical prediction because of the optimal control, but also facilitate the connectivity of distant atomic qubits in a larger atom array by reducing the requirement of strong blockade, which is promising for developing multiqubit quantum computation in large-scale atomic arrays.

quant-ph↗