arXiv subjects

Yufan Li

Publications and source records attributed to Yufan Li.

At least 19 recordsLinked to original sources

arXiv2026

High-Resolution Dynamical Eigenspectroscopy via Variational Trotter Compression on a Superconducting Qubit Processor

The pursuit of high-resolution eigenspectroscopy on noisy intermediate-scale quantum devices is often hindered by the trade-off between circuit depth and coherence time. In this work, we introduce and experimentally demonstrate a dynamical eigenspectroscopy protocol that extracts fine-grained energy structures from time-dependent survival amplitudes. To overcome the finite coherence window of current superconducting processors, we employ Variational Trotter Compression (VTC) as a practical means to extend the duration of high-fidelity unitary evolution. Using a multi-connected 9-qubit superconducting processor, we reconstruct the time-domain autocorrelation signal via quantum state tomography for the H2 molecule at different bond lengths and for the Fermi-Hubbard model across different correlation regimes. Through multi-frequency fitting and Fourier analysis, the extracted eigenenergies agree with the exact-diagonalization values to within 2x10^-3, including the near-degenerate levels in the strongly interacting regime. Our results establish experimental dynamical spectroscopy as a robust and generalizable framework for simulating both quantum chemistry and strongly correlated lattice systems, bridging weak- and strong-coupling regimes on near-term quantum hardware.

quant-ph↗

arXiv2026

Electronic Band Structure of Silicon Determined via a Variational Adiabatic Eigensolver: Theory and Experiment

This work addresses the critical challenge of excited-state preparation for semiconductor band structure calculations. We introduce a variational adiabatic eigensolver (VAE) protocol that combines adiabatic evolution with variational optimization to prepare high-fidelity eigenstates on noisy intermediate-scale quantum (NISQ) devices. Applying a momentum-space truncation, we accurately compute the electronic band structure of silicon -- an idealized infinite periodic system -- using only a modest number of qubits. Our approach employs multi-qubit parameterized circuits and a phase-based loss function, overcoming limitations of conventional methods. These limitations include the circuit-construction difficulty in traditional adiabatic approaches and the reduced accuracy of variational quantum eigensolvers for excited states. Through rigorous numerical simulation and experimental implementation on a superconducting quantum processor, we successfully prepare silicon's valence-band and conduction-band eigenstates. Single-shot readout yields state fidelities exceeding 96%, and the measured energy expectations agree with theoretical band energies within 0.5 eV. Further refinement via single-frequency oscillation fitting reduces the energy deviation to below 0.01 eV. This framework provides a robust and practical pathway for precisely determining electronic structures in quantum materials.

quant-ph↗

arXiv2026

3D Ising criticality with Platonic lattice superconducting qubits

The three-dimensional (3D) Ising model is a foundational model in statistical physics and critical phenomena, yet its analytical intractability has long impeded the precise determination of universal critical exponents. While high-precision estimates have been obtained through classical numerical methods and conformal bootstrap techniques, a direct quantum simulation of the 3D Ising criticality remains challenging, requiring nontrivial connectivity, sufficient system size, and high spectral resolution. In this work, assisted by the state-operator correspondence of conformal field theory, we perform a digital quantum simulation of the 3D Ising critical exponents using a multiply-connected 9-qubit superconducting quantum processor with a Platonic lattice geometry. Employing an extended variational quantum eigensolver equipped with a phase-based loss function, we variationally prepare the low-energy eigenstates of the transverse-field Ising model on a cubic Platonic lattice encoded in an 8-qubit register. The four lowest eigenenergies are extracted via Fourier-transform analysis and high-precision numerical fitting, agreeing with the exact diagonalization values up to +/- 0.001. The resulting scaling dimension Delta_epsilon = 1.5850 and critical exponent nu = 0.7067 match well with theory.

quant-ph↗

arXiv2026

Superconductivity in non-centrosymmetric rhombohedral NbSe2

Crystal stacking offers a powerful yet underexplored route to engineer symmetry in layered superconductors. Here we report superconductivity in rhombohedral-stacked NbSe2 (3R-NbSe2), a non-centrosymmetric polytype in which global inversion symmetry is removed by stacking alone. Using comprehensive structural, transport, magnetic, and thermodynamic measurements, we establish superconductivity as a bulk property of the 3R phase and find that the in-plane upper critical field exceeds the Pauli paramagnetic limit, indicating the persistence of strong Ising-type spin-orbit coupling. Unlike the thickness-dependent superconductivity in centrosymmetric 2H-NbSe2, the superconducting transition temperature in 3R-NbSe2 shows little dependence on layer number but exhibits an unusually strong sensitivity to disorder. We further observe strongly enhanced nonlinear optical and electrical responses near the superconducting transition, consistent with stacking-induced inversion-symmetry breaking. Our results identify 3R-NbSe2 as a single-phase platform in which stacking engineering reshapes superconductivity and enables nonlinear transport phenomena in layered materials.

cond-mat.supr-con↗

arXiv2025

Signature of chiral superconducting order parameter evidenced in mesoscopic superconductors

Chiral superconductivity is a novel superconducting phase characterized by order parameters that break the time-reversal symmetry, endowing the state with a definite handedness. Unlike conventional superconductors, the Cooper pairs in a chiral superconductor carry nonzero orbital angular momentum. Through coupling with an external magnetic field, the finite angular momentum of the Cooper pair modulates the temperature-magnetic field phase boundary in a distinctive way, which could serve as an experimental signature of the chiral superconducting state. Here we demonstrate that the chiral signature can be detected in mesoscopic superconducting rings of $\beta$-Bi$_2$Pd, manifesting as a linear-in-field modulation of the critical temperature in the Little-Parks effect. Our findings establish a new experimental method for detecting the chiral superconductivity.

cond-mat.supr-con↗

arXiv2025

Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling

We study the fundamental problem of calibrating a linear binary classifier of the form $\sigma(\hat{w}^\top x)$, where the feature vector $x$ is Gaussian, $\sigma$ is a link function, and $\hat{w}$ is an estimator of the true linear weight $w^\star$. By interpolating with a noninformative $\textit{chance classifier}$, we construct a well-calibrated predictor whose interpolation weight depends on the angle $\angle(\hat{w}, w_\star)$ between the estimator $\hat{w}$ and the true linear weight $w_\star$. We establish that this angular calibration approach is provably well-calibrated in a high-dimensional regime where the number of samples and features both diverge, at a comparable rate. The angle $\angle(\hat{w}, w_\star)$ can be consistently estimated. Furthermore, the resulting predictor is uniquely $\textit{Bregman-optimal}$, minimizing the Bregman divergence to the true label distribution within a suitable class of calibrated predictors. Our work is the first to provide a calibration strategy that satisfies both calibration and optimality properties provably in high dimensions. Additionally, we identify conditions under which a classical Platt-scaling predictor converges to our Bregman-optimal calibrated solution. Thus, Platt-scaling also inherits these desirable properties provably in high dimensions.

math.ST↗

arXiv2024

A robust, fiber-coupled scanning probe magnetometer using electron spins at the tip of a diamond nanobeam

Fiber-coupled sensors are well suited for sensing and microscopy in hard-to-reach environments such as biological or cryogenic systems. We demonstrate fiber-based magnetic imaging based on nitrogen-vacancy (NV) sensor spins at the tip of a fiber-coupled diamond nanobeam. We incorporated angled ion implantation into the nanobeam fabrication process to realize a small ensemble of NV spins at the nanobeam tip. By gluing the nanobeam to a tapered fiber, we created a robust and transportable probe with optimized optical coupling efficiency. We demonstrate the imaging capability of the fiber-coupled nanobeam by measuring the magnetic field generated by a current-carrying wire. With its robust coupling and efficient readout at the fiber-coupled interface, our probe could allow new studies of (quantum) materials and biological samples.

cond-mat.mes-hall↗

arXiv2024

Staying vigilant in the Age of AI: From content generation to content authentication

This paper presents the Yangtze Sea project, an initiative in the battle against Generative AI (GAI)-generated fake con-tent. Addressing a pressing issue in the digital age, we investigate public reactions to AI-created fabrications through a structured experiment on a simulated academic conference platform. Our findings indicate a profound public challenge in discerning such content, highlighted by GAI's capacity for realistic fabrications. To counter this, we introduce an innovative approach employing large language models like ChatGPT for truthfulness assess-ment. We detail a specific workflow for scrutinizing the authenticity of everyday digital content, aimed at boosting public awareness and capability in identifying fake mate-rials. We apply this workflow to an agent bot on Telegram to help users identify the authenticity of text content through conversations. Our project encapsulates a two-pronged strategy: generating fake content to understand its dynamics and developing assessment techniques to mitigate its impact. As part of that effort we propose the creation of speculative fact-checking wearables in the shape of reading glasses and a clip-on. As a computational media art initiative, this project under-scores the delicate interplay between technological progress, ethical consid-erations, and societal consciousness.

cs.CY↗

arXiv2024

ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data

Two key tasks in high-dimensional regularized regression are tuning the regularization strength for accurate predictions and estimating the out-of-sample risk. It is known that the standard approach -- $k$-fold cross-validation -- is inconsistent in modern high-dimensional settings. While leave-one-out and generalized cross-validation remain consistent in some high-dimensional cases, they become inconsistent when samples are dependent or contain heavy-tailed covariates. As a first step towards modeling structured sample dependence and heavy tails, we use right-rotationally invariant covariate distributions -- a crucial concept from compressed sensing. In the proportional asymptotics regime where the number of features and samples grow comparably, which is known to better reflect the empirical behavior in moderately sized datasets, we introduce a new framework, ROTI-GCV, for reliably performing cross-validation under these challenging conditions. Along the way, we propose new estimators for the signal-to-noise ratio and noise variance. We conduct experiments that demonstrate the accuracy of our approach in a variety of synthetic and semi-synthetic settings.

math.ST↗

arXiv2024

Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis

In the transfer learning paradigm models learn useful representations (or features) during a data-rich pretraining stage, and then use the pretrained representation to improve model performance on data-scarce downstream tasks. In this work, we explore transfer learning with the goal of optimizing downstream performance. We introduce a simple linear model that takes as input an arbitrary pretrained feature transform. We derive exact asymptotics of the downstream risk and its \textit{fine-grained} bias-variance decomposition. We then identify the pretrained representation that optimizes the asymptotic downstream bias and variance averaged over an ensemble of downstream tasks. Our theoretical and empirical analysis uncovers the surprising phenomenon that the optimal featurization is naturally sparse, even in the absence of explicit sparsity-inducing priors or penalties. Additionally, we identify a phase transition where the optimal pretrained representation shifts from hard selection to soft selection of relevant features.

stat.ML↗

arXiv2024

Size Ramsey numbers of small graphs versus fans or paths

For two graphs $G_1$ and $G_2$, the size Ramsey number $\hat{r}(G_1,G_2)$ is the smallest positive integer $m$ for which there exists a graph $G$ of size $m$ such that for any red-blue edge-coloring of the graph $G$, $G$ contains either a red subgraph isomorphic to $G_1$, or a blue subgraph isomorphic to $G_2$. Let $P_n$ be a path with $n$ vertices, $nK_2$ a matching with $n$ edges, and $F_n$ a graph with $n$ triangles sharing exactly one vertex. If $G_1$ is a small fixed graph and $G_2$ denotes any graph from a graph class, one can sometimes completely determine $\hat{r}(G_1,G_2)$. Faudree and Sheehan confirmed all size Ramsey numbers of $P_3$ versus complete graphs in 1983. The next year Erdős and Faudree confirmed that of $2K_2$ versus complete graphs and complete bipartite graphs. We obtain three more Ramsey results of this type. For $n\ge 3$, we prove that $\hat{r}(P_3,F_n)=4n+4$ if $n$ is odd, and $\hat{r}(P_3,F_n)=4n+5$ if $n$ is even. This result refutes a conjecture proposed by Baskoro et al. We also show that $\hat{r}(2K_2,F_2)=12$ and $\hat{r}(2K_2,F_n)=5n+3$ for $n\ge 3$. In addition, we prove that $\hat{r}(2K_2,nP_m)=\min\{nm+1, (n+1)(m-1)\}$. This result verifies a conjecture posed by Vito and Silaban.

math.CO↗

arXiv2023

Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression

Debiasing is a fundamental concept in high-dimensional statistics. While degrees-of-freedom adjustment is the state-of-the-art technique in high-dimensional linear regression, it is limited to i.i.d. samples and sub-Gaussian covariates. These constraints hinder its broader practical use. Here, we introduce Spectrum-Aware Debiasing--a novel method for high-dimensional regression. Our approach applies to problems with structured dependencies, heavy tails, and low-rank structures. Our method achieves debiasing through a rescaled gradient descent step, deriving the rescaling factor using spectral information of the sample covariance matrix. The spectrum-based approach enables accurate debiasing in much broader contexts. We study the common modern regime where the number of features and samples scale proportionally. We establish asymptotic normality of our proposed estimator (suitably centered and scaled) under various convergence notions when the covariates are right-rotationally invariant. Such designs have garnered recent attention due to their crucial role in compressed sensing. Furthermore, we devise a consistent estimator for its asymptotic variance. Our work has two notable by-products: first, we use Spectrum-Aware Debiasing to correct bias in principal components regression (PCR), providing the first debiased PCR estimator in high dimensions. Second, we introduce a principled test for checking alignment between the signal and the eigenvectors of the sample covariance matrix. This test is independently valuable for statistical methods developed using approximate message passing, leave-one-out, or convex Gaussian min-max theorems. We demonstrate our method through simulated and real data experiments. Technically, we connect approximate message passing algorithms with debiasing and provide the first proof of the Cauchy property of vector approximate message passing (V-AMP).

math.ST↗

arXiv2023

Quantum paramagnetism in a non-Kramers rare-earth oxide: Monoclinic $\rm Pr_2Ti_2O_7$

Little is so far known about the magnetism of the $\rm A_2B_2O_7$ monoclinic layered perovskites that replace the spin-ice supporting pyrochlore structure for $r_A/r_B>1.78$. We show that high quality monoclinic Pr$_2$Ti$_2$O$_7$ single crystals with a three-dimensional network of non-Kramers Pr$^{3+}$ ions that interact through edge-sharing super-exchange interactions, form a singlet ground state quantum paramagnet that does not undergo any magnetic phase transitions down to at least 1.8 K. The chemical phase stability, structure, and magnetic properties of the layered perovskite Pr$_2$Ti$_2$O$_7$ were investigated using x-ray diffraction, transmission electron microscopy, and magnetization measurements. Synthesis of polycrystalline samples with the nominal compositions of Pr$_2$Ti$_{2+x}$O$_7$ ($-0.16 \leq x \leq 0.16$) showed that deviations from the Pr$_2$Ti$_2$O$_7$ stoichiometry lead to secondary phases of related, structures including the perovskite phase Pr$_{2/3}$TiO$_3$ and the orthorhombic phases Pr$_4$Ti$_9$O$_{24}$ and Pr$_2$TiO$_5$. No indications of site disordering (stuffing and anti-stuffing) or vacancy defects were observed in the Pr$_2$Ti$_2$O$_7$ majority phase. A procedure for growth of high-structural-quality, stoichiometric single crystals of Pr$_2$Ti$_2$O$_7$ by the traveling solvent floating zone (TSFZ) method is reported. Thermo-magnetic measurements of single-crystalline Pr$_2$Ti$_2$O$_7$ reveal an isolated singlet ground state that we associate with the low symmetry crystal electric field environments that split the $2J+1=9$-fold degenerate spin-orbital multiplets of the four differently coordinated Pr$^{3+}$ ions into 36 isolated singlets resulting in an anisotropic temperature independent van-Vleck susceptibility at low $T$. A small isotropic Curie term is associated with 0.96(2)\% non-interacting Pr$^{4+}$ impurities.

cond-mat.mtrl-sci↗

arXiv2023

Balancing Risk and Reward: An Automated Phased Release Strategy

Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a principled way requires selecting the proportion of units assigned to the new release in a way that balances the risk of an adverse effect with the need to iterate and learn from the experiment rapidly. In this paper, we formalize this problem and propose an algorithm that automatically determines the release percentage at each stage in the schedule, balancing the need to control risk while maximizing ramp-up speed. Our framework models the challenge as a constrained batched bandit problem that ensures that our pre-specified experimental budget is not depleted with high probability. Our proposed algorithm leverages an adaptive Bayesian approach in which the maximal number of units assigned to the treatment is determined by the posterior distribution, ensuring that the probability of depleting the remaining budget is low. Notably, our approach analytically solves the ramp sizes by inverting probability bounds, eliminating the need for challenging rare-event Monte Carlo simulation. It only requires computing means and variances of outcome subsets, making it highly efficient and parallelizable.

stat.ML↗

arXiv2023

A Fiber-coupled Scanning Magnetometer with Nitrogen-Vacancy Spins in a Diamond Nanobeam

Magnetic imaging with nitrogen-vacancy (NV) spins in diamond is becoming an established tool for studying nanoscale physics in condensed matter systems. However, the optical access required for NV spin readout remains an important hurdle for operation in challenging environments such as millikelvin cryostats or biological systems. Here, we demonstrate a scanning-NV sensor consisting of a diamond nanobeam that is optically coupled to a tapered optical fiber. This nanobeam sensor combines a natural scanning-probe geometry with high-efficiency through-fiber optical excitation and readout of the NV spins. We demonstrate through-fiber optically interrogated electron spin resonance and proof-of-principle magnetometry operation by imaging spin waves in an yttrium-iron-garnet thin film. Our scanning-nanobeam sensor can be combined with nanophotonic structuring to control the light-matter interaction strength, and has potential for applications that benefit from all-fiber sensor access such as millikelvin systems.

cond-mat.mes-hall↗

arXiv2022

TAP equations for orthogonally invariant spin glasses at high temperature

We study the high-temperature regime of a mean-field spin glass model whose couplings matrix is orthogonally invariant in law. The magnetization of this model is conjectured to satisfy a system of TAP equations, originally derived by Parisi and Potters using a diagrammatic expansion of the Gibbs free energy. We prove that this TAP description is correct in an $L^2$ sense, in a regime of sufficiently high temperature. Our approach develops a novel geometric argument for proving the convergence of an Approximate Message Passing (AMP) algorithm to the magnetization vector, which is applicable in models without i.i.d. couplings. This convergence is shown via a conditional second moment analysis of the free energy restricted to a thin band around the output of the AMP algorithm, in a system of many "orthogonal" replicas.

math.PR↗

arXiv2022

Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature

We study estimation in the linear model $y=Aβ^\star+ε$, in a Bayesian setting where $β^\star$ has an entrywise i.i.d. prior and the design $A$ is rotationally-invariant in law. In the large system limit as dimension and sample size increase proportionally, a set of related conjectures have been postulated for the asymptotic mutual information, Bayes-optimal mean squared error, and TAP mean-field equations that characterize the Bayes posterior mean of $β^\star$. In this work, we prove these conjectures for a general class of signal priors and for arbitrary rotationally-invariant designs $A$, under a "high-temperature" condition that restricts the range of eigenvalues of $A^\top A$. Our proof uses a conditional second-moment method argument, where we condition on the iterates of a version of the Vector AMP algorithm for solving the TAP mean-field equations.

cs.IT↗

arXiv2022

Unequivocal Determination of Spin-Triplet Superconductivity Using Composite Rings

Phase-sensitive measurements on a composite ring made of a superconductor of interest connected by a known singlet $s$-wave superconductor can unambiguously determine its pairing symmetry. In composite rings with epitaxial $β-$Bi$_2$Pd and $s$-wave Nb, we have observed half-integer quantum flux when Nb is connected to the opposite crystalline ends of $β-$Bi$_2$Pd and integer-quantum flux when Nb is connected to the same crystalline ends of $β-$Bi$_2$Pd. These findings provide unequivocal evidence of odd-parity pairing state of the triplet superconductor $β-$Bi$_2$Pd.

cond-mat.supr-con↗