arXiv subjects

Qian Ding

Publications and source records attributed to Qian Ding.

7 recordsLinked to original sources

arXiv2025

Normal form computation of nonlinear dispersion relationship for locally resonant metamaterial

This article is devoted to the application of the parametrisation method for invariant manifold with a complex normal form style (CNF), for the derivation of high-order approximations of underdamped nonlinear dispersion relationships for periodic structures, more specifically by considering the case of a locally resonant metamaterial chain incorporating damping and various nonlinear stiffnesses. Two different strategies are proposed to solve the problem. In the first one, Bloch's assumption is first applied to the equations of motion, and then the nonlinear change of coordinates provided by the complex normal form style in the parametrisation method is applied. This direct procedure, which applies first the wave dependency to the original physical coordinates of the problem, is referred to as CNF-BP (for CNF applied with Bloch's assumption on physical coordinates). In the second strategy, the nonlinear change of coordinates provided by the parametrisation method, which relates the physical coordinates to the so-called normal coordinates, is first applied. Then the periodic assumption is used, thus imposing a Bloch wave ansatz on the normal coordinates. This method will be referred to as CNF-PN (for CNF with a periodic assumption on normal coordinates). In the conservative case, the CNF-PN strategy exhibits superior capability in capturing complex wave propagation phenomena, whereas the CNF-BP strategy encounters limitations in handling non-fundamental harmonics and the nonlinear interactions between host oscillators. For underdamped systems, the CNF-PN is rigorously validated and systematically compared against numerical techniques, a classical analytical perturbation technique (the method of multiple scales), and direct numerical time integration of annular chain structures.

physics.optics↗

arXiv2025

Hardware Co-Designed Optimal Control for Programmable Atomic Quantum Processors via Reinforcement Learning

Developing scalable, fault-tolerant atomic quantum processors requires precise control over large arrays of optical beams. This remains a major challenge due to inherent imperfections in classical control hardware, such as inter-channel crosstalk and beam leakage. In this work, we introduce a hardware co-designed intelligent quantum control framework to address these limitations. We construct a mathematical model of the photonic control hardware, integrate it into the quantum optimal control (QOC) framework, and apply reinforcement learning (RL) techniques to discover optimal control strategies. We demonstrate that the proposed framework enables robust, high-fidelity parallel single-qubit gate operations under realistic control conditions, where each atom is individually addressed by an optical beam. Specifically, we implement and benchmark three optimization strategies: a classical hybrid Self-Adaptive Differential Evolution-Adam (SADE-Adam) optimizer, a conventional RL approach based on Proximal Policy Optimization (PPO), and a novel end-to-end differentiable RL method. Using SADE-Adam as a baseline, we find that while PPO performance degrades as system complexity increases, the end-to-end differentiable RL consistently achieves gate fidelities above 99.9$\%$, exhibits faster convergence, and maintains robustness under varied channel crosstalk strength and randomized dynamic control imperfections.

quant-ph↗

arXiv2024

Measuring Human Contribution in AI-Assisted Content Generation

With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

cs.CY↗

arXiv2024

Intelligent Reflecting Surfaces vs. Full-Duplex Relays: A Comparison in the Air

This letter aims to provide a fundamental analytical comparison for the two major types of relaying methods: intelligent reflecting surfaces and full-duplex relays, particularly focusing on unmanned aerial vehicle communication scenarios. Both amplify-and-forward and decode-and-forward relaying schemes are included in the comparison. In addition, optimal 3D UAV deployment and minimum transmit power under the quality of service constraint are derived. Our numerical results show that IRSs of medium size exhibit comparable performance to AF relays, meanwhile outperforming DF relays under extremely large surface size and high data rates.

eess.SP↗

arXiv2024

Towards the THz Networks in the 6G Era

This commentary dedicates to envision what role THz is going to play in the coming human-centric 6G era. Three distinct THz network types including outdoor, indoor, and body area networks are discussed, with an emphasis on their capabilities in human body detection. Synthesizing these networks will unlock a bunch of fascinating applications across industrial, biomedical and entertainment fields, significantly enhancing the quality of human life.

eess.SY↗

arXiv2023

A Generalizable TCAD Framework for Silicon FinFET Spin Qubit Devices with Electrical Control

We present a TCAD-based simulation framework established for quantum dot spin qubits in a silicon FinFET platform with all-electrical control of the spin state. The framework works down to 1K and consists of a two-step simulation chain, from definition of the quantum dot confinement potential with DC bias voltages, to calculation of microwave response electric field at qubit locations using small-signal AC analysis. An average field polarization vector at each quantum dot is extracted via a post-processing step. We demonstrate functionality of this approach by simulation of a recently reported two-qubit device in the form of a 5-gate silicon FinFET. The impact of the number of holes in each quantum dot on the MW response E-field polarization direction is further investigated for this device. The framework is easily generalizable to study future multi-qubit large-scale systems.

cond-mat.mes-hall↗

arXiv2011

On Nonlinear Stochastic Balance Laws

We are concerned with multidimensional stochastic balance laws. We identify a class of nonlinear balance laws for which uniform spatial $BV$ bounds for vanishing viscosity approximations can be achieved. Moreover, we establish temporal equicontinuity in $L^1$ of the approximations, uniformly in the viscosity coefficient. Using these estimates, we supply a multidimensional existence theory of stochastic entropy solutions. In addition, we establish an error estimate for the stochastic viscosity method, as well as an explicit estimate for the continuous dependence of stochastic entropy solutions on the flux and random source functions. Various further generalizations of the results are discussed.

math.AP↗