arXiv subjects

Daw-Wei Wang

Publications and source records attributed to Daw-Wei Wang.

At least 19 recordsLinked to original sources

arXiv2026

Channel concentration of critical quantum geometry

The quantum metric quantifies the total ground-state response along a parameter direction, but does not resolve how excitations share that response. We define channel concentration (CC) as the sum of squared normalized response weights over specified excitation channels, such as momentum blocks. An exact finite-size theorem yields thermodynamic CCs of 2/3 for the field response of the critical transverse-field Ising model and 1/3 for the half-filled XX pairing response, despite the same leading metric scaling. In finite-size approaches to the XY Lifshitz point, field and anisotropy perturbations yield different concentrations despite a common limiting Hamiltonian with quadratic dispersion. In a unitary 1+1-dimensional conformal field theory (CFT) on a circle, we consider a nondegenerate vacuum in a fixed sector perturbed by one spatially integrated scalar primary. We derive complete zero-momentum energy-level response weights, including descendants. For scaling dimension $0<Δ<3/2$, these weights determine the normalized response distribution and an exact universal concentration function. The expression reproduces the exact Ising lattice limit 2/3 and gives approximately 0.8515 for the three-state Potts thermal field, compared with approximately 0.800 from an exponent-only approximation. Finite-size interacting calculations compare concentrations and ranked response weights over many-body energy levels. These exact benchmarks show which response distinctions total metric scaling misses and guide comparisons with finite-size interacting spectra.

cond-mat.stat-mech↗

arXiv2026

Efficient identification of critical regions via Flow Matching-based Monte Carlo initialization

Markov chain Monte Carlo (MCMC) is a standard tool for studying many-body systems, but its practical cost can become substantial, especially when simulations must be repeated across temperatures and lattice sizes or near transition regions where equilibration becomes increasingly difficult. In this work, we introduce a Flow Matching (FM) framework. It is not a standalone replacement for equilibrium Monte Carlo. Instead, we present it as a scalable, physically informed initializer for downstream MCMC simulations. We use a U-Net architecture. The FM model is trained on small-system configurations of the 2D XY model and then deployed across unseen temperatures and larger lattice sizes. FM-generated configurations preserve the correct qualitative physical trends across temperature and system size. This makes them suitable warm-start states for subsequent Monte Carlo refinement. Observables computed from FM-generated samples primarily serve as diagnostics of initializer quality, not as precision equilibrium estimates. The regression-based $L_2$ objective suppresses variance and limits the accuracy of fluctuation-sensitive observables. Examples include susceptibility and spin stiffness. Still, the model captures sufficient local statistical structure to yield physically aligned initial states across a broad range of conditions. These results support a reusable hybrid FM--MCMC workflow. The one-time FM training cost can be amortized across temperatures and lattice sizes. The generated warm-start configurations then reduce the burden of initializing large-scale Monte Carlo simulations. Our findings show that Flow Matching can support efficient exploration of transition regions in many-body systems by providing reusable warm-start configurations for downstream Monte Carlo simulations.

cond-mat.stat-mech↗

arXiv2026

General Mechanism of Evolution Shared by Proteins and Words

Complex systems, such as life and languages, are governed by principles of evolution. The analogy and comparison between biology and linguistics\cite{alphafold2, RoseTTAFold, lang_virus, cell language, faculty1, language of gene, Protein linguistics, dictionary, Grammar of pro_dom, complexity, genomics_nlp, InterPro, language modeling, Protein language modeling} provide a computational foundation for characterizing and analyzing protein sequences, human corpora, and their evolution. However, no general mathematical formula has been proposed so far to illuminate the origin of quantitative hallmarks shared by life and language. Here we show several new statistical relationships shared by proteins and words, which inspire us to establish a general mechanism of evolution with explicit formulations that can incorporate both old and new characteristics. We found natural selection can be quantified via the entropic formulation by the principle of least effort to determine the sequence variation that survives in evolution. Besides, the origin of power law behavior and how changes in the environment stimulate the emergence of new proteins and words can also be explained via the introduction of function connection network. Our results demonstrate not only the correspondence between genetics and linguistics over their different hierarchies but also new fundamental physical properties for the evolution of complex adaptive systems. We anticipate our statistical tests can function as quantitative criteria to examine whether an evolution theory of sequence is consistent with the regularity of real data. In the meantime, their correspondence broadens the bridge to exchange existing knowledge, spurs new interpretations, and opens Pandora's box to release several potentially revolutionary challenges. For example, does linguistic arbitrariness conflict with the dogma that structure determines function?

q-bio.PE↗

arXiv2025

A Unsupervised Framework for Identifying Diverse Quantum Phase Transitions Using Classical Shadow Tomography

We provide a general machine learning methodology that integrates classical shadow representations with unsupervised principal component analysis (PCA) to explore various quantum phase transitions. By sampling spin configurations from random Pauli measurements, our approach can effectively analyze hidden statistical patterns in the data, thereby capturing the distinct signatures of quantum criticality through their fluctuations. We benchmark this approach across various spin-1/2 systems, including the 1D XZX cluster-Ising model, the 1D bond-alternating XXZ model, the 2D transverse-field Ising model, and the 2D Kitaev honeycomb model. We show that PCA not only reliably detects and distinguishes both symmetry-breaking and topological transitions, but also enables their qualitative classification based on characteristic fluctuation patterns. Our data-driven approach does not require any knowledge of the Hamiltonian or explicit order parameters, and can therefore be a general and applicable tool for probing new quantum phases.

quant-ph↗

arXiv2025

Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning

Accurate determination of qubit parameters is critical for the successful implementation of quantum information and computation applications. In solid state systems, the parameters of individual qubits vary across the entire system, requiring time consuming measurements and manual fitting processes for characterization. Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, offer improved fidelity operations but exhibit a more complex physical and spectral structure, complicating parameter extraction. In this work, we propose a machine learning (ML)based methodology for the automatic and accurate characterization of fluxonium qubit parameters. Our approach utilized the energy spectrum calculated by a model Hamiltonian with various magnetic fields, as training data for the ML model. The output consists of the essential fluxonium qubit energy parameters, EJ, EC, and EL in Hamiltonian. The ML model achieves remarkable accuracy (with an average accuracy 95.6%) as an initial guess, enabling the development of an automatic fitting procedure for direct application to realistic experimental data. Moreover, we demonstrate that similar accuracy can be retrieved even when the input experimental spectrum is noisy or incomplete, highlighting the model robustness. These results suggest that our automated characterization method, based on a transfer learning approach, provides a reliable framework for future extensions to other superconducting qubits or different solid-state systems. Ultimately, we believe this methodology paves the way for the construction of large-scale quantum processors.

quant-ph↗

arXiv2023

Self-Supervised Ensemble Learning: A Universal Method for Phase Transition Classification of Many-Body Systems

We develop a self-supervised ensemble learning (SSEL) method to accurately classify distinct types of phase transitions by analyzing the fluctuation properties of machine learning outputs. Employing the 2D Potts model and the 2D Clock model as benchmarks, we demonstrate the capability of SSEL in discerning first-order, second-order, and Berezinskii-Kosterlitz-Thouless transitions, using in-situ spin configurations as the input features. Furthermore, we show that the SSEL approach can also be applied to investigate quantum phase transitions in 1D Ising and 1D XXZ models upon incorporating quantum sampling. We argue that the SSEL model simulates a special state function with higher-order correlations between physical quantities, and hence provides richer information than previous machine learning methods. Consequently, our SSEL method can be generally applied to the identification/classification of phase transitions even without explicit knowledge of the underlying theoretical models.

cond-mat.stat-mech↗

arXiv2022

Determine the Core Structure and Nuclear Equation of State of Rotating Core-Collapse Supernovae with Gravitational Waves by Convolutional Neural Networks

Detecting gravitational waves from a nearby core-collapse supernova would place meaningful constraints on the supernova engine and nuclear equation of state. Here we use Convolutional Neural Network models to identify the core rotational rates, rotation length scales, and the nuclear equation of state (EoS), using the 1824 waveforms from Richers et al. (2017) for a 12 solar mass progenitor. High prediction accuracy for the classifications of the rotation length scales ($93\%$) and the rotational rates ($95\%$) can be achieved using the gravitational wave signals from -10 ms to 6 ms core bounce. By including additional 48 ms signals during the prompt convection phase, we could achieve $96\%$ accuracy on the classification of four major EoS groups. Combining three models above, we could correctly predict the core rotational rates, rotation length scales, and the EoS at the same time with more than $85\%$ accuracy. Finally, applying a transfer learning method for additional 74 waveforms from FLASH simulations (Pan et al. 2018), we show that our model using Richers' waveforms could successfully predict the rotational rates from Pan's waveforms even for a continuous value with a mean absolute errors of 0.32 rad s$^{-1}$ only. These results demonstrate a much broader parameter regimes our model can be applied for the identification of core-collapse supernova events through GW signals.

astro-ph.HE↗

arXiv2022

2D Gapless Topological Superfluids Generated by Pairing Phases

We systematically investigate the ground state phase diagram and the finite temperature phase transitions for a Rydberg-dressed Fermi gas loaded in a bilayer optical lattice. When an effective finite-ranged attraction is induced, our self-consistent mean-field calculation shows that the gapped topological ( $p$-wave) superfluids in each layer are coupled together by the $s$-wave pairing in an intermediate inter-layer distance with a spontaneously modulated phases between these two order parameters. The obtained ground state is a gapless topological superfluid with quantized topological charges characterizing the gapless points, leading to a zero energy flat band at the edges. Finally, we calculate the finite temperature phase diagrams of this two-dimensional gapless superfluid and observe two distinct critical temperatures, demonstrating the fruitful many-body effects on a paired topological superfluids.

cond-mat.quant-gas↗

arXiv2021

Two-dimensional Paired Topological Superfluids of Rydberg Fermi Gases

We systematically investigate the topological properties of spin polarized Rydberg-dressed fermionic atoms loaded in a bilayer optical lattice. Through tuning the Rydberg coupling strength and the inter-layer tunneling amplitude, we identify different types of topological superfluid states generated from the inter-layer pairing and relative gauge phase modulation of the couples 2D $p$-wave superfluids. These phases includes gapped/gapless with/without time reversal symmetry. One of the most interesting states is a gapless paired topological superfluid with both the time-reversal symmetry and particle-hole symmetry. This state is equivalent to a topological Kondo lattice model with the spin-orbit coupling, an in-plane magnetic field, and an additional particle-hole symmetry. The flexibility of experimental manipulation in such Rydberg-dressed ferminoic systems therefore becomes a promising system for realizing interesting topological superfluids.

cond-mat.supr-con↗

arXiv2021

Robust Identification of Topological Phase Transition by Self-Supervised Machine Learning Approach

We propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of ultracold atoms. Different from the conventional supervised learning approach, where the predicted phase transition point is very sensitive to the training region and data labeling, our self-supervised learning approach identifies the phase transition point by the largest deviation of the predicted results from the known system parameters and by the highest confidence through a systematic shift of the training regions. We demonstrate the robust application of this approach results in various 1D and 2D exactly solvable models, using different input features (time-of-flight images, spatial correlation function or density-density correlation function). As a result, our self-supervised approach should be a very general and reliable method for many condensed matter or solid-state systems to observe new states of matters solely based on experimental measurements, even without a priori knowledge of the phase transition models.

cond-mat.quant-gas↗

arXiv2021

Searching for Young Stellar Objects through SEDs by Machine Learning

Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, is essential for constraining star formation theories. However, it is a difficult task to separate galaxies and YSOs based on spectral energy distributions (SEDs) alone, because they contain both thermal emission from stars and dust around them and no reliable theories can be applied to distinguish them. Here we compare different machine learning algorithms and develop the Spectrum Classifier of Astronomical Objects (SCAO), based on Fully Connected Neural Network (FCN), to classify regular stars, galaxies, and YSOs. Superior to previous classifiers, SCAO is solely trained by high quality data labeled in Molecular Cores to Planet-forming Disks (c2d) catalog without a priori theoretical knowledge, and provides excellent results with high precision (>96%) and recall (>98%) for YSOs when only eight bands are included. We systematically investigate the effects of observation errors and distance effects, and show that high accuracy performance is still maintained even when using fluxes of only three bands (IRAC 3, IRAC 4, and MIPS 1) in the long wavelengths regime, because the silicate absorption feature is automatically detected by SCAO. Finally, we apply SCAO to Spitzer Enhanced Imaging Products (SEIP), the most complete catalog of Spitzer observations, and found 129219 YSO candidates. The website from SCAO is available at http://scao.astr.nthu.edu.tw.

astro-ph.SR↗

arXiv2020

Random Sampling Neural Network for Quantum Many-Body Problems

The eigenvalue problem of quantum many-body systems is a fundamental and challenging subject in condensed matter physics, since the dimension of the Hilbert space (and hence the required computational memory and time) grows exponentially as the system size increases. A few numerical methods have been developed for some specific systems, but may not be applicable in others. Here we propose a general numerical method, Random Sampling Neural Networks (RSNN), to utilize the pattern recognition technique for the random sampling matrix elements of an interacting many-body system via a self-supervised learning approach. Several exactly solvable 1D models, including Ising model with transverse field, Fermi-Hubbard model, and spin-$1/2$ $XXZ$ model, are used to test the applicability of RSNN. Pretty high accuracy of energy spectrum, magnetization and critical exponents etc. can be obtained within the strongly correlated regime or near the quantum phase transition point, even the corresponding RSNN models are trained in the weakly interacting regime. The required computation time scales linearly to the system size. Our results demonstrate that it is possible to combine the existing numerical methods for the training process and RSNN to explore quantum many-body problems in a much wider parameter regime, even for strongly correlated systems.

cond-mat.dis-nn↗

arXiv2020

Identification of Neuronal Polarity by Node-Based Machine Learning

Identify the directions of signal flows in neural networks is one of the most important stages for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in different regions of Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained by nodal information only and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of the blowfly, which has much less neuronal data available. Our results demonstrate that NPIN is a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain's neural networks.

q-bio.NC↗

arXiv2020

Self-organizing Pattern in Multilayer Network for Words and Syllables

One of the ultimate goals for linguists is to find universal properties in human languages. Although words are generally considered as representing arbitrary mapping between linguistic forms and meanings, we propose a new universal law that highlights the equally important role of syllables, which is complementary to Zipf's. By plotting rank-rank frequency distribution of word and syllable for English and Chinese corpora, visible lines appear and can be fit to a master curve. We discover the multi-layer network for words and syllables based on this analysis exhibits the feature of self-organization which relies heavily on the inclusion of syllables and their connections. Analytic form for the scaling structure is derived and used to quantify how Internet slang becomes fashionable, which demonstrates its usefulness as a new tool to evolutionary linguistics.

cs.CL↗

arXiv2019

Quantum Degenerate Majorana Surface Zero Modes in Two-Dimensional Space

We investigate the topological properties of spin polarized fermionic polar molecules loaded in a multi-layer structure with the electric dipole moment polarized to the normal direction. When polar molecules are paired by attractive inter-layer interaction, unpaired Majorana fermions can be macroscopically generated in the top and bottom layers in dilute density regime. We show that the resulting topological state is effectively composed by a bundle of 1D Kitaev ladders labeled by in-plane momenta k and -k, and hence belongs to BDI class characterized by the winding number Z, protected by the time reversal symmetry. The Majorana surface modes exhibit a flatband at zero energy, fully gapped from Bogoliubov excitations in the bulk, and hence becomes an idea system to investigate the interaction effects on quantum degenerate Majorana fermions. We further show that additional interference fringes can be identified as a signature of such 2D Majorana surface modes in the time-of-flight experiment.

cond-mat.quant-gas↗

arXiv2017

Fulde-Ferrell-Larkin-Ovchinnikov state in bilayer dipolar systems

We study the phase diagram of fermionic polar molecules in a bilayer system, with an imbalance of molecular densities of the layers. For the imbalance exceeding a critical value the system undergoes a transition from the uniform interlayer superfluid to the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state with a stripe structure, and at sufficiently large imbalance a transition from the FFLO to normal phase. Compared to the case of contact interactions, the FFLO regime is enhanced by the long-range character of the interlayer dipolar interaction, which can combine the s-wave and p-wave pairing in the order parameter.

cond-mat.quant-gas↗

arXiv2017

Quantitative Studies on the Quantum Critical Regime near Superfluid to Mott Insulator Transition

We investigate the critical behaviors of correlation length and critical exponents for strongly interacting bosons in a two-dimensional optical lattice via quantum Monte Carlo simulations. By comparing the full numerical results to those given by the effective theory, we quantitatively determine the critical regime where the universal scaling behaviors applies for both classical Berezinskii-Kosterlitz-Thouless transition at a finite temperature and quantum phase transition from superfluid to Mott insulator. Our results show that the critical regime can be as large as a few lattice sites in optical lattice and should be observable in present experimental conditions.

cond-mat.quant-gas↗

arXiv2017

Phases of dipolar bosons in a bilayer geometry

We study by first principle computer simulations the low temperature phase diagram of bosonic dipolar gases in a bilayer geometry, as a function of the two control parameters, i.e., the in-plane density and the interlayer distance. We observe four distinct phases, namely paired and decoupled superfluids, as well as a crystal of dimers and one consisting of two aligned crystalline layers. A direct quantum phase transition from a dimer crystal to two independent superfluids is observed in a relatively wide range of parameters. No supersolid phase is predicted for this system.

cond-mat.quant-gas↗