arXiv subjects

Yiyuan Luo

Publications and source records attributed to Yiyuan Luo.

7 recordsLinked to original sources

arXiv2026

Interface-Confined Superconductivity with Thickness-Independent Superfluid Stiffness in (Pb,Sn)Te/FeTe Bilayers

Interface-induced superconductivity in FeTe-based heterostructures provides a promising route toward topological superconductivity, yet the roles of the neighboring layers topology, symmetry, and electronic structure remain unresolved. In this work, we employ molecular beam epitaxy to grow Pb1-xSnxTe/FeTe bilayers and use angle-resolved photoemission spectroscopy to track the evolution of the Pb1-xSnxTe layer from a trivial insulator to a topological crystalline insulator hosting multiple Dirac surface states. Electrical transport measurements reveal robust superconductivity throughout the entire composition range, with a nearly constant superconducting transition temperature of ~12 K despite substantial changes in the electronic structure and topology of Pb1-xSnxTe. Double-coil mutual-inductance measurements further reveal comparable superfluid stiffness across the topological phase transition and nearly thickness-independent superfluid stiffness despite large variations in the constituent-layer thicknesses, demonstrating that superconductivity is confined near the interface. These results establish that superconductivity in FeTe-based heterostructures is largely insensitive to the topology, crystal symmetry, and detailed electronic structure of the neighboring layer, supporting a primary origin in modifications to the FeTe layer. The coexistence of interface-confined superconductivity and tunable multiple Dirac surface states in Pb1-xSnxTe/FeTe bilayers provides a versatile platform for exploring topological superconductivity and interactions among multiple Majorana zero modes.

cond-mat.supr-con↗

arXiv2026

Strong Refutation of Ordering, Phylogenetic, and Ordinary CSPs, and New Satisfiability and Refutation Thresholds for Triplet and Quartet Reconstruction

We study phase transitions and algorithms for refuting CSPs arising in hierarchical clustering (as well as ranking, and ordinary CSPs). Here, $n$ variables are assigned to leaves of a tree, so as to satisfy $m$ constraints, specifying evolutionary relationships. Two canonical $NP$-hard optimization problems are Triplet and Quartet Reconstruction, where the input consists of triplets $xy|z$ or quartets $xy|zw$, and the goal is to find a tree $T^*$ maximizing agreement with constraints. Our main results are (as density $λ=m/n$ increases): 1. We show the existence and precisely locate the sharp threshold $λ^*\approx1.2277$ for Triplets (via closed-form solution). To the best of our knowledge, this is the first sharp threshold for the broad family of Phylogenetic CSPs. Moreover, we give a lower and upper bound for Quartets. 2. We provide strong refutation algorithms that certify that $val(T^*)\le5/9 + ε$, where $val(T^*)$ is the fraction of constraints satisfied by the (unknown) optimal tree. For triplets, our algorithm succeeds w.h.p if $m =Ω(n)$, and for quartets if $m = Ω(n^{3/2})$. 3. We obtain strongest possible refutations at slightly larger densities (for triplets $m=O(n^{3/2}\log ^3n)$, for quartets $m=O(n^2)$): we certify that $T^*$ is no better than a random assignment, i.e., $val(T^*)\le 1/3+ε$. In fact, we obtain strongest possible refutations for finite-alphabet CSPs with or without negations. Our refutations above are instantiations of our general theorem that applies more broadly to Phylogenetic and Ordering CSPs (and all CSPs failing to support $t$-wise independence), and generalizes the current algorithmic frontier on refuting random CSPs~\citep{allen2015refute}. A crucial difference here, unlike Boolean CSPs, is that there are no negated variables, so prior works relying on negations -- a source of randomness -- do not apply.

cs.DS↗

arXiv2026

Collective quantum state at the atomic limit

Collective quantum states are often associated with extended systems, where spatially extensive degrees of freedom enable emergent many-body behavior; whether such strongly correlated states survive at atomic dimensions remains a fundamental question. Tomonaga-Luttinger liquids provide a paradigmatic example of one-dimensional collective quantum matter characterized by spin-charge separation. Using low-temperature scanning tunneling microscopy and spectroscopy, we directly visualize quantized collective modes in atomically confined mirror twin boundary segments of monolayer WSe2. Distinct standing-wave branches associated with fractionalized spin and charge excitations persist in segments as short as one nanometer, establishing the atomic-scale confinement limit of Luttinger-liquid behavior. These ultrashort segments form a new class of many-body quantum dots whose discrete spectra arise from confined collective bosonic modes rather than single-particle electron states. When assembled into ordered chains, inter-dot coupling reshapes electron-like fundamental states while collective spin/charge excitations remain largely intact, revealing distinct coupling responses of emergent many-body modes. Our results demonstrate that collective quantum matter can persist and exhibit fundamentally distinct coupling behavior at atomic length scales, establishing a novel platform for engineering strongly correlated quantum phases from atomically confined building blocks.

cond-mat.mes-hall↗

arXiv2026

Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch

Embedding-based representations in Euclidean space $\mathbb{R}^d$ are a cornerstone of modern machine learning, where a major goal is to use the \emph{smallest dimension} that faithfully captures data relations. In this work, we prove sharp dimension--accuracy tradeoffs and identify a fundamental information-theoretic limitation: unless the embedding dimension $d$ is chosen close to the ground-truth dimension $D$, accuracy undergoes a sudden collapse. Our main result shows that this phenomenon arises even in standard contrastive learning settings, where supervision is limited to a set of $m$ anchor--positive--negative triplets $(i,j,k)$ encoding distance comparisons $\mathrm{dist}(i,j) < \mathrm{dist}(i,k)$. Specifically, given triplets realizable by an unknown ground-truth embedding in $D$ dimensions, we prove that there exists constant $c < 1$, such that \emph{every embedding of dimension at most $cD$ violates half of the triplets}, yielding accuracy as low as a trivial one-dimensional solution that ignores the input. We complement our information-theoretic bounds with strong computational hardness results: under the Unique Games Conjecture, even if the given triplets are nearly realizable in $D=1$ dimension, no polynomial-time algorithm -- \textit{regardless of its dimension} -- can achieve accuracy above the trivial $50\%$ baseline.

cs.DS↗

arXiv2026

Optimal Phylogenetic Reconstruction from Sampled Quartets

Quartet Reconstruction, the task of recovering a phylogenetic tree from smaller trees on four species called \textit{quartets}, is a well-studied problem in theoretical computer science with far-reaching connections to statistics, graph theory and biology. Given a random sample containing $m$ noisy quartets, labeled by an unknown ground-truth tree $T$ on $n$ taxa, we want to output a tree $\widehat T$ that is \textit{close} to $T$ in terms of quartet distance and can predict unseen quartets. Unfortunately, the empirical risk minimizer corresponds to the $\mathsf{NP}$-hard problem of finding a tree that maximizes agreements with the sampled quartets, and earlier works in approximation algorithms gave $(1-\eps)$-approximation schemes (PTAS) for dense instances with $m=Θ(n^4)$ quartets, or for $m=Θ(n^2\log n)$ quartets \textit{randomly} sampled from $T$. Prior to our work, it was unknown how many samples are information-theoretically required to learn the tree, and whether there is an efficient reconstruction algorithm. We present optimal results for reconstructing an unknown phylogenetic tree $T$ from a random sample of $m=Θ(n)$ quartets, corrupted under the Random Classification Noise (RCN) model. This matches the $Ω(n)$ lower bound required for any meaningful tree reconstruction. Our contribution is twofold: first, we give a tree reconstruction algorithm that, not only achieves a $(1-\eps)$-approximation, but most importantly \textit{recovers} a tree close to $T$ in quartet distance; second, we show a new $Θ(n)$ bound on the Natarajan dimension of phylogenies (an analog of VC dimension in multiclass classification). Our analysis relies on a new \textit{Quartet-based Embedding and Detection} procedure that identifies and removes well-clustered subtrees from the (unknown) ground-truth $T$ via semidefinite programming.

cs.DS↗

arXiv2025

The Complexity of Finding Local Optima in Contrastive Learning

Contrastive learning is a powerful technique for discovering meaningful data representations by optimizing objectives based on $\textit{contrastive information}$, often given as a set of weighted triplets $\{(x_i, y_i^+, z_{i}^-)\}_{i = 1}^m$ indicating that an "anchor" $x_i$ is more similar to a "positive" example $y_i$ than to a "negative" example $z_i$. The goal is to find representations (e.g., embeddings in $\mathbb{R}^d$ or a tree metric) where anchors are placed closer to positive than to negative examples. While finding $\textit{global}$ optima of contrastive objectives is $\mathsf{NP}$-hard, the complexity of finding $\textit{local}$ optima -- representations that do not improve by local search algorithms such as gradient-based methods -- remains open. Our work settles the complexity of finding local optima in various contrastive learning problems by proving $\mathsf{PLS}$-hardness in discrete settings (e.g., maximize satisfied triplets) and $\mathsf{CLS}$-hardness in continuous settings (e.g., minimize Triplet Loss), where $\mathsf{PLS}$ (Polynomial Local Search) and $\mathsf{CLS}$ (Continuous Local Search) are well-studied complexity classes capturing local search dynamics in discrete and continuous optimization, respectively. Our results imply that no polynomial time algorithm (local search or otherwise) can find a local optimum for various contrastive learning problems, unless $\mathsf{PLS}\subseteq\mathsf{P}$ (or $\mathsf{CLS}\subseteq \mathsf{P}$ for continuous problems). Even in the unlikely scenario that $\mathsf{PLS}\subseteq\mathsf{P}$ (or $\mathsf{CLS}\subseteq \mathsf{P}$), our reductions imply that there exist instances where local search algorithms need exponential time to reach a local optimum, even for $d=1$ (embeddings on a line).

cs.LG↗

arXiv2021

Two Problems about Monomial Bent Functions

In 2008, Langevin and Leander determined the dual function of three classes of monomial bent functions with the help of Stickelberger's theorem: Dillon, Gold and Kasami. In their paper, they proposed one very strong condition such that their method works, and showed that both Gold exponent and Kasami exponent satisfy this condition. In 2018, Pott {\em et al.} investigated the issue of vectorial functions with maximal number of bent components. They found one class of binomial functions which attains the upper bound. They also proposed an open problem regarding monomial function with maximal number of bent components. In this paper, we obtain an interesting result about the condition of Langevin and Leander, and solve the open problem of Pott {\em et al.}. Specifically, we show that: 1) for a monomial bent function over $\mathbb{F}_{2^{2k}}$, if the exponent satisfies the first part of the condition of Langevin and Leander, then it satisfies the entire condition; 2) $x^{2^k+1}$ is the only monomial function over $\mathbb{F}_{2^{2k}}$ which has maximal number of bent components. Fortunately, as a consequence, we also solve an open problem of Ness and Helleseth in 2006.

cs.IT↗