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Zhiming Huang

Publications and source records attributed to Zhiming Huang.

15 recordsLinked to original sources

Comparison of Two-Atom Cross Spectra in de Sitter Spacetime, Uniformly Accelerated Minkowski Vacuum, and a Thermal Bath

We study the weak coupling of two identical two-level atoms to a four-dimensional massless conformally coupled scalar field and compare three settings with the same local temperature but different physical origins: comoving atoms in the Bunch--Davies vacuum of de Sitter spacetime, transversely separated uniformly accelerated atoms in the Minkowski vacuum, and static atoms in a Minkowski thermal bath. We first derive the two-atom cross spectra in the three settings and then obtain the single-atom local spectra uniformly from the zero-separation limit. When $a=H$ and $\beta=2\pi/H$, the complete local Wightman kernels and local spectra are identical in all three cases. At finite separation, a genuine thermal bath produces a $\sinc(\Omega L)$ spatial factor, whereas de Sitter spacetime and the transversely accelerated vacuum produce a hyperbolic geometric factor. In the long-distance limit, the cross correlations in de Sitter spacetime and the transversely accelerated vacuum decay as $L^{-2}$, whereas those in the Minkowski thermal bath decay only as $L^{-1}$. The de Sitter and transversely accelerated vacuum cross spectra coincide when $a=H$ and the separations are instantaneously matched. For a fixed experimental setup, the physical separation between the two comoving atoms in de Sitter space evolves with cosmic expansion, whereas the pulled-back cross correlations in the uniformly accelerated and thermal Minkowski configurations are stationary.

gr-qc

Instance-Adaptive Online Multicalibration

We study online multicalibration beyond the worst-case. We give a single, efficient algorithm which dynamically interpolates between benign and worst-case sequences by adaptively refining a dyadic grid of prediction values. Its error is controlled by the number of leaves in the refinement tree. Our analysis recovers the known $\widetilde O(T^{2/3})$ worst-case-optimal rate for online multicalibration, while simultaneously automatically adapting to easier instances: in the marginal stochastic setting it obtains a rate of $\widetilde O(\sqrt T)$, and for piecewise-stationary means with $J$ segments its rate is $\widetilde O(\sqrt{JT})$. More generally, the rate depends on a threshold-complexity measure of the predictable mean process relative to the group family. We show that this dependence is tight up to logarithmic factors.

cs.LG

Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits

We revisit combinatorial Thompson sampling (CTS) for semi-bandits with sleeping arms, where arm availability varies over time and actions must satisfy combinatorial constraints, as in wireless mesh routing with fluctuating link availability. Despite its practical relevance, CTS has been hindered by several long-standing problems: (i) the absence of worst-case regret guarantees in the semi-bandit setting even without sleeping arms, (ii) the lack of theory under adversarially varying availability, and (iii) the consistently weak empirical performance of CTS with Gaussian priors (CTS-G). This paper resolves these long-standing issues by providing the first worst-case regret analysis of CTS-G, proving an upper bound of $\tilde{O}(m\sqrt{NT})$ and a matching lower bound of $\tilde{\Omega}(m\sqrt{NT})$. To bridge the gap between theory and practice, we further propose CL-SG, a simple CTS-G variant that samples a single shared Gaussian seed each round to coordinate exploration across arms. We show that CL-SG achieves an improved regret bound of $\tilde{O}(\sqrt{mNT})$, together with a matching lower bound $\Omega(\sqrt{mNT})$. Experiments on real-world datasets demonstrate that CL-SG consistently outperforms strong baselines including CTS-G and CTS-B, and we open-source our implementation for reproducibility.

cs.LG

Stochastic Geometry of Cylinders: Characterizing Inter-Nodal Distances for 3D UAV Networks

The analytical characterization of coverage probability in finite three-dimensional wireless networks has long remained an open problem, hindered by the loss of spatial independence in finite-node settings and the coupling between link distances and interference in bounded geometries. This paper closes this gap by presenting the first exact analytical framework for coverage probability in finite 3D networks modeled by a binomial point process within a cylindrical region. To bypass the intractability that has long hindered such analyses, we leverage the independence structure, convolution geometry, and derivative properties of Laplace transforms, yielding a formulation that is both mathematically exact and computationally efficient. Extensive Monte Carlo simulations verify the analysis and demonstrate significant accuracy gains over conventional Poisson-based models. The results generalize to any confined 3D wireless system, including aerial, underwater, and robotic networks.

cs.NI

Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret

We address differentially private stochastic bandit problems from the angles of exploring the deep connections among Thompson Sampling with Gaussian priors, Gaussian mechanisms, and Gaussian differential privacy (GDP). We propose DP-TS-UCB, a novel parametrized private bandit algorithm that enables to trade off privacy and regret. DP-TS-UCB satisfies $ \tilde{O} \left(T^{0.25(1-\alpha)}\right)$-GDP and enjoys an $O \left(K\ln^{\alpha+1}(T)/\Delta \right)$ regret bound, where $\alpha \in [0,1]$ controls the trade-off between privacy and regret. Theoretically, our DP-TS-UCB relies on anti-concentration bounds of Gaussian distributions and links exploration mechanisms in Thompson Sampling-based algorithms and Upper Confidence Bound-based algorithms, which may be of independent interest.

cs.LG

Near-Optimal Algorithms for Differentially Private Online Learning in a Stochastic Environment

In this paper, we study differentially private online learning problems in a stochastic environment under both bandit and full information feedback. For differentially private stochastic bandits, we propose both UCB and Thompson Sampling-based algorithms that are anytime and achieve the optimal $O \left(\sum_{j: Δ_j>0} \frac{\ln(T)}{\min \left\{Δ_j, ε\right\}} \right)$ instance-dependent regret bound, where $T$ is the finite learning horizon, $Δ_j$ denotes the suboptimality gap between the optimal arm and a suboptimal arm $j$, and $ε$ is the required privacy parameter. For the differentially private full information setting with stochastic rewards, we show an $Ω\left(\frac{\ln(K)}{\min \left\{Δ_{\min}, ε\right\}} \right)$ instance-dependent regret lower bound and an $Ω\left(\sqrt{T\ln(K)} + \frac{\ln(K)}ε\right)$ minimax lower bound, where $K$ is the total number of actions and $Δ_{\min}$ denotes the minimum suboptimality gap among all the suboptimal actions. For the same differentially private full information setting, we also present an $ε$-differentially private algorithm whose instance-dependent regret and worst-case regret match our respective lower bounds up to an extra $\log(T)$ factor.

cs.LG

Efficient and Adaptive Posterior Sampling Algorithms for Bandits

We study Thompson Sampling-based algorithms for stochastic bandits with bounded rewards. As the existing problem-dependent regret bound for Thompson Sampling with Gaussian priors [Agrawal and Goyal, 2017] is vacuous when $T \le 288 e^{64}$, we derive a more practical bound that tightens the coefficient of the leading term %from $288 e^{64}$ to $1270$. Additionally, motivated by large-scale real-world applications that require scalability, adaptive computational resource allocation, and a balance in utility and computation, we propose two parameterized Thompson Sampling-based algorithms: Thompson Sampling with Model Aggregation (TS-MA-$α$) and Thompson Sampling with Timestamp Duelling (TS-TD-$α$), where $α\in [0,1]$ controls the trade-off between utility and computation. Both algorithms achieve $O \left(K\ln^{α+1}(T)/Δ\right)$ regret bound, where $K$ is the number of arms, $T$ is the finite learning horizon, and $Δ$ denotes the single round performance loss when pulling a sub-optimal arm.

cs.LG

Semiquantum key distribution using initial states in only one basis without the classical user measuring

From the perspective of resource theory, it is interesting to achieve the same quantum task using as few quantum resources as possible. Semiquantum key distribution (SQKD), which allows a quantum user to share a confidential key with a classical user who prepares and operates qubits in only one basis, is an important example for studying this issue. To further limit the quantum resources used by users, in this paper, we constructed the first SQKD protocol which restricts the quantum user to prepare quantum states in only one basis and removes the classical user's measurement capability. Furthermore, we prove that the constructed protocol is unconditionally secure by deriving a key rate expression of the error rate in the asymptotic scenario. The work of this paper provides inspiration for achieving quantum superiority with minimal quantum resources.

quant-ph

The extended quasi-Einstein manifolds with generalised Ricci solitons

As a generalization of Einstein manifolds, the nearly quasi-Einstein manifolds and pseudo quasi-Einstein manifolds are both interesting and useful in studying the general relativity. In this paper, we study the extended quasi-Einstein manifolds which derive from pseudo quasi-Einstein manifolds. After showing the existence theorem of extended quasi-Einstein manifold, we give some special geometric properties of such manifolds. At the same time, we also discuss the extended quasi-Einstein manifolds with certain soliton like generalised Ricci soliton or Riemann soliton. Furthermore, we construct some nontrivial example to illustrate these extended quasi-Einstein manifolds.

math.DG

Room-Temperature Anisotropic Plasma Mirror and Polarization-Controlled Optical Switch Based on Type-II Weyl Semimetal WP2

Anisotropy in electronic structures may ignite intriguing anisotropic optical responses, as has been well demonstrated in various systems including superconductors, semiconductors, and even topological Weyl semimetals. Meanwhile, it is well established in metal optics that the metal reflectance declines from one to zero when the photon frequency is above the plasma frequency ωp , behaving as a plasma mirror. However, the exploration of anisotropic plasma mirrors and corresponding applications remains elusive, especially at room temperature. Here, we discover a pronounced anisotropic plasma reflectance edge in the type-II Weyl semimetal WP2, with an anisotropy ratio of ωp up to 1.5. Such anisotropic plasma mirror behavior and its robustness against temperature promise optical device applications over a wide temperature range. For example, the high sensitivity of polarization-resolved plasma reflectance edge renders WP2 an inherent polarization detector. We further achieve a room-temperature WP2-based optical switch, effectively controlled by simply tuning the light polarization. These findings extend the frontiers of metal optics as a discipline and promise the design of multifunctional devices combining both topological and optical features.

cond-mat.mtrl-sci

Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints

We study the combinatorial sleeping multi-armed semi-bandit problem with long-term fairness constraints~(CSMAB-F). To address the problem, we adopt Thompson Sampling~(TS) to maximize the total rewards and use virtual queue techniques to handle the fairness constraints, and design an algorithm called \emph{TS with beta priors and Bernoulli likelihoods for CSMAB-F~(TSCSF-B)}. Further, we prove TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by $\frac{N}{2η} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, where $N$ is the total number of arms, $m$ is the maximum number of arms that can be pulled simultaneously in each round~(the cardinality constraint) and $η$ is the parameter trading off fairness for rewards. By relaxing the fairness constraints (i.e., let $η\rightarrow \infty$), the bound boils down to the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Finally, we perform numerical experiments and use a high-rating movie recommendation application to show the effectiveness and efficiency of the proposed algorithm.

cs.LG

Quantum entanglement for atoms coupling to fluctuating electromagnetic field in the cosmic string spacetime

We investigate entanglement dynamics for two atoms coupling with fluctuating electromagnetic field in the cosmic string spacetime. We calculate the entanglement for different conditions. It is found that the entanglement behaviors are dependent on vacuum fluctuation, spacetime topology, two-atom separation and atomic polarization orientation. After a long time of evolution, entanglement would vanish, which means entanglement affected by electromagnetic fluctuation can not maintain for a long time. For different spacetime topologies, entanglement presents different behaviors dependent on various parameters. When deficit angle parameter $ν=1$ and atom-string distance is towards infinity, the results in flat spacetime are recovered. When atoms keep close to the string, entanglement can be improved; specially, when two atoms locate on the string and have no polarization of axial direction, atoms are not affected by the electromagnetic fluctuation and entanglement can remain unchanged. When two-atom separation is relatively large, entanglement exhibits oscillation behavior as atom-string distance varies. This indicates that the existence of string profoundly modifies on the vacuum fluctuation and atom-field interaction. In addition, when two-atom separation is small, entanglement gains better improvement. Many parameters and conditions provide us with greater freedom to control the entanglement behaviors. In principle, this is useful to sense the cosmic string spacetime topology structure and property, and discriminate different kinds of spacetime.

quant-ph

Entropic uncertainty in the background of expanding de Sitter space-time

We study the dynamics of quantum-memory-assisted entropic uncertainty for a hybrid qutrit-qubit system interacting with fluctuating quantum scalar field in the background of expanding de Sitter space. We firstly derive the master equation that the system evolution obeys. As evolution time goes by, for different initial states, entropic uncertainty develops to different fixed values for different parameter values, whereas entanglement always decays to zero, and there exist monotonous relations between entropic uncertainty, entanglement and various parameters for a fixed initial state, but mixedness behaves differently with entropic uncertainty and entanglement. Further it is found that the entropic uncertainty closely associated with the entanglement and mixedness. In addition, it is shown that the entropic uncertainty can be manipulated effectively via the weak measurement reversal. Our study would give some useful insights about the behavior characteristics of high dimensional quantum system in expanding de Sitter space-time, and may be useful to the tasks of quantum information processing of curved space-time since the uncertainty principle plays vital role in quantum information science and technology.

quant-ph

Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks

Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special network can take interference into consideration, which is likely to be an efficient way to describe the underlying mechanism. With the assistance of belief entropy, named as Deng entropy, the paper proposes Belief Distance to render the model practical. Tested with empirical data, the proposed model is proved to be predictable and effective.

cs.AI

Quantum coherence behaviors of fermionic system in non-inertial frame

In this paper, we analyse the quantum coherence behaviors of a single qubit in the relativistic regime beyond the single-mode approximation. Firstly, we investigate the freezing condition of quantum coherence in fermionic system. We also study the quantum coherence tradeoff between particle and antiparticle sector. It is found that there exists quantum coherence transfer between particle and antiparticle sector, but the coherence lost in particle sector is not entirely compensated by the coherence generation of antiparticle sector. Besides, we emphatically discuss the cohering power and decohering power of Unruh channel with respect to the computational basis. It is shown that cohering power is vanishing and decohering power is dependent of the choice of Unruh mode and acceleration. Finally, We compare the behaviors of quantum coherence with geometric quantum discord and entanglement in relativistic setup. Our results show that this quantifiers in two region converge at infinite acceleration limit, which implies that this measures become independent of Unruh modes beyond the single-mode approximations. It is also demonstrated that the robustness of quantum coherence and geometric quantum discord are better than entanglement under the influence of acceleration, since entanglement undergoes sudden death.

quant-ph