arXiv subjects

Yi-Ting Ma

Publications and source records attributed to Yi-Ting Ma.

3 recordsLinked to original sources

arXiv2025

Polarization Entanglement in Atomic Biphotons via OAM-to-Spin Mapping

We demonstrate polarization-entangled biphotons in a cold-atom double-$Λ$ system, overcoming atomic selection rules that suppress polarization correlations and favor orbital angular momentum (OAM) entanglement. Using spatial light modulators, we coherently map a selected two-dimensional OAM subspace onto the polarization basis and thereby open an otherwise inaccessible polarization channel. Quantum-state tomography confirms that the mapping preserves the biphoton coherence. The four polarization Bell states are generated with fidelities of $92\text{-}94\%$ with few-percent statistical uncertainties, and an average Clauser-Horne-Shimony-Holt parameter of $S=2.44$ verifies the survival of nonlocal correlations. To the best of our knowledge, this work presents the first demonstration of OAM-to-polarization entanglement transfer in a cold-atom spontaneous four-wave mixing platform and establishes a practical interface for integrating atomic OAM resources with polarization-based quantum communication networks.

quant-ph↗

arXiv2025

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs

We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies. However, SRRW's reliance on explicit computation of transition probabilities for all neighbors at each step introduces substantial computational overhead, while its strict dependence on time-reversible Markov chains excludes advanced non-reversible MCMC methods. To overcome these limitations, instead of direct modification of transition kernel, HDT introduces a history-dependent target distribution $\boldsymbolπ[\mathbf{x}]$ to replace the original target $\boldsymbolμ$ in any graph sampler, where $\mathbf{x}$ represents the empirical measure of past visits. This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\boldsymbolμ$ and near-zero variance performance. Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used (LRU) cache ensures scalability to large general graphs.

cs.LG↗

arXiv2024

Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD

Distributed learning is essential to train machine learning algorithms across heterogeneous agents while maintaining data privacy. We conduct an asymptotic analysis of Unified Distributed SGD (UD-SGD), exploring a variety of communication patterns, including decentralized SGD and local SGD within Federated Learning (FL), as well as the increasing communication interval in the FL setting. In this study, we assess how different sampling strategies, such as i.i.d. sampling, shuffling, and Markovian sampling, affect the convergence speed of UD-SGD by considering the impact of agent dynamics on the limiting covariance matrix as described in the Central Limit Theorem (CLT). Our findings not only support existing theories on linear speedup and asymptotic network independence, but also theoretically and empirically show how efficient sampling strategies employed by individual agents contribute to overall convergence in UD-SGD. Simulations reveal that a few agents using highly efficient sampling can achieve or surpass the performance of the majority employing moderately improved strategies, providing new insights beyond traditional analyses focusing on the worst-performing agent.

cs.LG↗