arXiv subjects

Feng Pan

Publications and source records attributed to Feng Pan.

At least 37 records · Page 2Linked to original sources

arXiv2026

Branch-and-Bound Tensor Networks for Exact Ground-State Characterization

Characterizing the ground-state properties of disordered systems, such as spin glasses and combinatorial optimization problems, is fundamental to science and engineering. However, computing exact ground states and counting their degeneracies are generally NP-hard and #P-hard problems, respectively, posing a formidable challenge for exact algorithms. Recently, Tensor Networks methods, which utilize high-dimensional linear algebra and achieve massive hardware parallelization, have emerged as a rapidly developing paradigm for efficiently solving these tasks. Despite their success, these methods are fundamentally constrained by the exponential growth of space complexity, which severely limits their scalability. To address this bottleneck, we introduce the Branch-and-Bound Tensor Network (BBTN) method, which seamlessly integrates the adaptive search framework of branch-and-bound with the efficient contraction of tropical tensor networks, significantly extending the reach of exact algorithms. We show that BBTN significantly surpasses existing state-of-the-art solvers, setting new benchmarks for exact computation. It pushes the boundaries of tractability to previously unreachable scales, enabling exact ground-state counting for $\pm J$ spin glasses up to $64 \times 64$ and solving Maximum Independent Set problems on King's subgraphs up to $100 \times 100$. For hard instances, BBTN dramatically reduces the computational cost of standard Tropical Tensor Networks, compressing years of runtime into minutes. Furthermore, it outperforms leading integer-programming solvers by over 30$\times$, establishing a versatile and scalable framework for solving hard problems in statistical physics and combinatorial optimization.

cond-mat.stat-mech↗

arXiv2025

Anomalous collective modes in atomic nuclei within the proton-neutron interacting boson model

Novel collective modes characterized by a $B_{4/2}$ ratio ($\equiv B(E2;4_1^+\rightarrow 2_1^+)/B(E2;2_1^+\rightarrow 0_1^+)$) less than 1.0 that were observed recently have been identified within the proton-neutron interacting boson model (IBM-2) using the consistent-$Q$ Hamiltonian. These modes are shown to give rise to triaxial spectral features, including significant band mixing. The results provide a compelling explanation for the deeply suppressed $B_{4/2}$ ratio observed in $^{166}$W, $^{168,170}$Os, and $^{172}$Pt, offering new insights into the $B(E2)$ anomaly phenomenon in neutron-deficient nuclei.

nucl-th↗

arXiv2025

Room-temperature valley-selective emission in Si-MoSe2 heterostructures enabled by high-quality-factor chiroptical cavities

Transition metal dichalcogenides possess valley pseudospin, enabling coupling between photon spin and electron spin for classical and quantum information processing. However, rapid valley-dephasing processes have impeded the development of scalable, high-performance valleytronic devices operating at room temperature. Here we demonstrate that a chiral resonant metasurface can enable room-temperature valley-selective emission in MoSe2 monolayers independent of excitation polarization. This platform provides circular eigen-polarization states with a high quality factor (Q-factor) and strong chiral near-field enhancement. The fabricated Si chiral metasurfaces exhibit chiroptical resonances with Q-factors up to 450 at visible wavelengths. We reveal degrees of circular polarization (DOP) reaching a record high of 0.5 at room temperature. Our measurements show that the high DOP can be attributed to the significantly increased chiroptical local density of states, which enhances valley-specific radiative transition rates by a factor of ~13. Our work could facilitate the development of ultracompact chiral classical and quantum light sources.

physics.optics↗

arXiv2025

Triaxial rotor modes in finite-N boson systems

We propose an algebraic approach to elucidate the dynamic characteristics of triaxial rotor modes in nuclei by mapping a triaxial rotor Hamiltonian to the interacting boson model (IBM) one within a finite-$N$ framework. Our method unveils striking features not observed in conventional modes, exemplified by the $B(E2)$ anomaly, characterized by $B(E2;4_1--2_1^+)/B(E2;2_1--0_1^+)<<1$. Using specific examples, we demonstrate that the peculiar properties of low-lying states in both neutron-rich and neutron-deficient Os nuclei can be comprehensively understood through the proposed Hamiltonian, which incorporates both rigid and soft triaxial rotor modes. This algebraic method not only offers fresh insights into triaxial dynamics but also showcases its capability in uncovering emergent exotic collective modes in nuclear structure.

nucl-th↗

arXiv2025

Pose-Free 3D Quantitative Phase Imaging of Flowing Cellular Populations

High-throughput 3D quantitative phase imaging (QPI) in flow cytometry enables label-free, volumetric characterization of individual cells by reconstructing their refractive index (RI) distributions from multiple viewing angles during flow through microfluidic channels. However, current imaging methods assume that cells undergo uniform, single-axis rotation, which require their poses to be known at each frame. This assumption restricts applicability to near-spherical cells and prevents accurate imaging of irregularly shaped cells with complex rotations. As a result, only a subset of the cellular population can be analyzed, limiting the ability of flow-based assays to perform robust statistical analysis. We introduce OmniFHT, a pose-free 3D RI reconstruction framework that leverages the Fourier diffraction theorem and implicit neural representations (INRs) for high-throughput flow cytometry tomographic imaging. By jointly optimizing each cell's unknown rotational trajectory and volumetric structure under weak scattering assumptions, OmniFHT supports arbitrary cell geometries and multi-axis rotations. Its continuous representation also allows accurate reconstruction from sparsely sampled projections and restricted angular coverage, producing high-fidelity results with as few as 10 views or only 120 degrees of angular range. OmniFHT enables, for the first time, in situ, high-throughput tomographic imaging of entire flowing cell populations, providing a scalable and unbiased solution for label-free morphometric analysis in flow cytometry platforms.

cs.CV↗

arXiv2025

Algebraic description of the triaxially to axially rotational shape phase transition

Within the framework of the interacting boson model, we propose a novel algebraic scheme to describe spin-dependent structural evolutions in triaxial nuclei. Our analysis demonstrates that a triaxially to axially rotational shape phase transition can be induced by the high-order correction which is microscopically motivated by the pairing interaction on the $γ$-unstable rotation. This prescription is further applied to describe the structural evolutions along the yrast sequences of $^{132}$Ba and $^{130}$Xe, providing an exemplary case for demonstrating a unified explanation of both low-energy collective structures and high-spin phenomena in O(6)-like nuclei.

nucl-th↗

arXiv2025

Integrating Neural Networks and Tensor Networks for Computing Free Energy

Computing free energy is a fundamental problem in statistical physics. Recently, two distinct methods have been developed and have demonstrated remarkable success: the tensor-network-based contraction method and the neural-network-based variational method. Tensor networks are accu?rate, but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems. The neural network method applies to systems with general topology. However, as a variational method, it is not as accurate as tensor networks. In this work, we propose an integrated approach, tensor-network-based variational autoregressive networks (TNVAN), that leverages the strengths of both tensor networks and neural networks: combining the variational autoregressive neural network's ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network's power to accurately compute the partition function for small sub-systems, resulting in a robust method for precisely estimating free energy. To evaluate the proposed approach, we conducted numerical experiments on spin glass systems with various topologies, including two-dimensional lattices, fully connected graphs, and random graphs. Our numerical results demonstrate the superior accuracy of our method compared to existing approaches. In particular, it effectively handles systems with long-range interactions and leverages GPU efficiency without requiring singular value decomposition, indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.

cond-mat.stat-mech↗

arXiv2025

SAT problem and Limit of Solomonoff's inductive reasoning theory

This paper explores the Boolean Satisfiability Problem (SAT) in the context of Kolmogorov complexity theory. We present three versions of the distinguishability problem-Boolean formulas, Turing machines, and quantum systems-each focused on distinguishing between two Bernoulli distributions induced by these computational models. A reduction is provided that establishes the equivalence between the Boolean formula version of the program output statistical prediction problem and the #SAT problem. Furthermore, we apply Solomonoff's inductive reasoning theory, revealing its limitations: the only "algorithm" capable of determining the output of any shortest program is the program itself, and any other algorithms are computationally indistinguishable from a universal computer, based on the coding theorem. The quantum version of this problem introduces a unique algorithm based on statistical distance and distinguishability, reflecting a fundamental limit in quantum mechanics. Finally, the potential equivalence of Kolmogorov complexity between circuit models and Turing machines may have significant implications for the NP vs P problem. We also investigate the nature of short programs corresponding to exponentially long bit sequences that can be compressed, revealing that these programs inherently contain loops that grow exponentially.

cs.CC↗

arXiv2025

Generative Decoding for Quantum Error-correcting Codes

Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical operators in high-rate codes. In this work, we propose a decoding algorithm utilizing generative modeling in machine learning. We employ autoregressive neural networks to learn the joint probability of logical operators and syndromes in an unsupervised manner, eliminating the need for labeled training data. The learned model can approximately perform maximum likelihood decoding by directly generating the most likely logical operators for $k$ logical qubits with $\mathcal O(2k)$ computational complexity. Thus, it is particularly efficient for decoding high-rate codes with many logical qubits. The proposed approach is general and applies to a wide spectrum of quantum error-correcting codes including surface codes and quantum low-density parity-check codes (qLDPC), under noise models ranging from code capacity noise to circuit level noise. We conducted extensive numerical experiments to demonstrate that our approach achieves significantly higher decoding accuracy compared to the minimum weight perfect matching and belief propagation with ordered statistics on the surface codes and high-rate quantum low-density parity-check codes. Our approach highlights generative artificial intelligence as a potential solution for the real-time decoding of realistic and high-rate quantum error correction codes.

quant-ph↗

arXiv2025

Deep learning approaches for nuclear binding energy prediction: a comparative study of RNN, GRU and LSTM Models

This study investigates the application of deep learning models-recurrent neural networks, gated recurrent units, and long short-term memory networks-for predicting nuclear binding energies. Utilizing data from the Atomic Mass Evaluation (AME2020), we incorporate key nuclear structure features, including proton and neutron numbers, as well as additional terms from the liquid drop model and shell effects. Our comparative analysis demonstrates that the gated recurrent units model achieves the lowest root-mean-square error (σRMSE) of 0.326 MeV, surpassing traditional regression-based approaches. To assess model reliability, we validate predictions using the GarveyKelson relations, obtaining an error of 0.202 MeV, and further test extrapolation capabilities using the WS, WS3, and WS4 models. The extrapolation analysis confirms the robustness of our approach, particularly in predicting binding energies for nuclei near the driplines. These results highlight the effectiveness of deep learning in nuclear BE predictions, highlighting its potential to enhance the accuracy and reliability of theoretical nuclear models.

nucl-th↗

arXiv2025

Discovery of a large magnetic nonlinear Hall effect in an altermagnet

Since Edwin Halls groundbreaking discovery of the Hall effect in 1879, magnetism, spin, and quantization have been expanding the scope of Hall effects, continuously driving transformative progress in science and technology. Among them, the latest nonlinear Hall effect (NLHE), where longitudinal electric field tunes quantum geometry to generate nonlinear Hall voltage, attracts wide attention as a sensitive probe of topological phases across a wide range of materials. Here, we report a new Hall effect member: the magnetic nonlinear Hall effect (MNLHE), characterized by a quadratic Hall conductivity dependence on magnetic field, rather than electric field as in NLHE. This finding relies on an altermagnet, Mn5Si3 thin film, whose alternating-sign Berry curvatures ensure higher-order MNLHE clearly distinguishable from the first-order anomalous Hall effect. The observed quadratic dependence originates from chiral next-nearest-neighbor hopping processes that acquire magnetic-exchange-driven Zeeman energies and Haldane-like chiral flux phases. Remarkably, this MNLHE is non-analytic, as reversing the magnetic field flips the alternating spin-splitting bands and reverses the hopping chirality, which is absent in traditional NLHE. Beyond offering a distinctive transport fingerprint for altermagnet Mn5Si3 thin film, this MNLHE is large and unsaturated up to 60 T, providing opportunities for pulsed high-field sensing technologies in both fundamental researches and engineering applications.

cond-mat.mtrl-sci↗

arXiv2025

Exact Decoding of Repetition Code under Circuit Level Noise

Repetition code forms a fundamental basis for quantum error correction experiments. To date, it stands as the sole code that has achieved large distances and extremely low error rates. Its applications span the spectrum of evaluating hardware limitations, pinpointing hardware defects, and detecting rare events. However, current methods for decoding repetition codes under circuit level noise are suboptimal, leading to inaccurate error correction thresholds and introducing additional errors in event detection. In this work, we establish that repetition code under circuit level noise has an exact solution, and we propose an optimal maximum likelihood decoding algorithm called planar. The algorithm is based on the exact solution of the spin glass partition function on planar graphs and has polynomial computational complexity. Through extensive numerical experiments, we demonstrate that our algorithm uncovers the exact threshold for depolarizing noise and realistic superconductor SI1000 noise. Furthermore, we apply our method to analyze data from recent quantum memory experiments conducted by Google Quantum AI, revealing that part of the error floor was attributed to the decoding algorithm used by Google. Finally, we implemented the repetition code quantum memory on superconducting systems with a 72-qubit quantum chip lacking reset gates, demonstrating that even with an unknown error model, the proposed algorithm achieves a significantly lower logical error rate than the matching-based algorithm.

quant-ph↗

arXiv2024

Electrical Manipulation of Spin Splitting Torque in Altermagnetic RuO2

Due to nonrelativistic altermagnetic spin splitting effect (ASSE), altermagnets can generate time-reversal-odd spin current and spin splitting torque (SST) with spin polarization parallel to the Néel vector. Hence the effective manipulation of SST would provide plenty of opportunities for designable spintronic devices, which remains elusive. Here, the electrical control of SST is achieved in altermagnetic RuO2, based on controllable Néel vector of RuO2 and Néel vector-dependent generation of SST. We demonstrate the current-induced switching of Néel vector via spin-orbit torque in RuO2 films, according to the reversible polarity of electrical transport measurements and X-ray magnetic linear dichroism (XMLD). The XMLD also unprecedentedly demonstrates that Néel vector really exists in altermagnets. The switching of Néel vector to the current direction and resultantly enhanced spin polarization parallel to the Néel vector brings about stronger ASSE-induced spin current. Our findings not only enrich the properties of altermagnets but also pave the way for high speed memories and nano-oscillators with excellent controllability and efficiency.

cond-mat.mtrl-sci↗

arXiv2024

Probing the Néel order in altermagnetic RuO2 films by X-ray magnetic linear dichroism

The emerging altermagnetic RuO2 with both compensated magnetic moments and broken time-reversal symmetry possesses nontrivial magneto-electronic responses and nonrelativistic spin currents, which are closely related to magnetic easy axis. To probe the Néel order in RuO2, we conducted Ru M3-edge X-ray magnetic linear dichroism (XMLD) measurement. For epitaxial RuO2 films, characteristic XMLD signals can be observed in either RuO2(100) and RuO2(110) at normal incidence or RuO2(001) at oblique incidence, and the signals disappear when test temperature exceeds Néel temperature. For nonepitaxial RuO2 films, the flat lines in the XMLD patterns of RuO2(100) and RuO2(110) demonstrate that there is no in-plane uniaxial alignment of Néel order in these samples, due to the counterbalanced Néel order of the twin crystals evidenced by X-ray diffraction phi-scan measurements. Our experimental results unambiguously demonstrate the antiferromagnetism in RuO2 films and reveal the spatial relation of Néel order to be parallel with RuO2 [001] crystalline axis. These research findings would deepen our understanding of RuO2 and other attractive altermagnetic materials applied in the field of spintronics.

cond-mat.mtrl-sci↗

arXiv2024

Superionic Ionic Conductor Discovery via Multiscale Topological Learning

Lithium superionic conductors (LSICs) are crucial for next-generation solid-state batteries, offering exceptional ionic conductivity and enhanced safety for renewable energy and electric vehicles. However, their discovery is extremely challenging due to the vast chemical space, limited labeled data, and the understanding of complex structure-function relationships required for optimizing ion transport. This study introduces a multiscale topological learning (MTL) framework, integrating algebraic topology and unsupervised learning to tackle these challenges efficiently. By modeling lithium-only and lithium-free substructures, the framework extracts multiscale topological features and introduces two topological screening metrics-cycle density and minimum connectivity distance-to ensure structural connectivity and ion diffusion compatibility. Promising candidates are clustered via unsupervised algorithms to identify those resembling known superionic conductors. For final refinement, candidates that pass chemical screening undergo ab initio molecular dynamics simulations for validation. This approach led to the discovery of 14 novel LSICs, four of which have been independently validated in recent experiments. This success accelerates the identification of LSICs and demonstrates broad adaptability, offering a scalable tool for addressing complex materials discovery challenges.

cond-mat.mtrl-sci↗

arXiv2024

Free-Energy Machine for Combinatorial Optimization

Finding optimal solutions to combinatorial optimization problems is pivotal in both scientific and technological domains, within academic research and industrial applications. A considerable amount of effort has been invested in the development of accelerated methods that leverage sophisticated models and harness the power of advanced computational hardware. Despite the advancements, a critical challenge persists, the dual demand for both high efficiency and broad generality in solving problems. In this work, we propose a general method, Free-Energy Machine (FEM), based on the ideas of free-energy minimization in statistical physics, combined with automatic differentiation and gradient-based optimization in machine learning. The algorithm is flexible, solving various combinatorial optimization problems using a unified framework, and is efficient, naturally utilizing massive parallel computational devices such as graph processing units (GPUs) and field-programmable gate arrays (FPGAs). We benchmark our algorithm on various problems including the maximum cut problems, balanced minimum cut problems, and maximum $k$-satisfiability problems, scaled to millions of variables, across both synthetic, real-world, and competition problem instances. The findings indicate that our algorithm not only exhibits exceptional speed but also surpasses the performance of state-of-the-art algorithms tailored for individual problems. This highlights that the interdisciplinary fusion of statistical physics and machine learning opens the door to delivering cutting-edge methodologies that will have broad implications across various scientific and industrial landscapes.

cond-mat.stat-mech↗

arXiv2024

Lead-free Hybrid Perovskite: An Efficient Room Temperature Spin Generator via Large Interfacial Rashba effect

Two-dimensional (2D) hybrid organic-inorganic perovskite (HOIP) demonstates great potential for developing flexible and wearable spintronic devices, by serving as spin sources via the bulk Rashba effect (BRE). However, the practical application of BRE in 2D HOIP faces huge challenges, particularly due to the toxicity of lead, which is crucial for achieving large spin-orbit coupling, and the restrictions in 2D HOIP candidates to meet specific symmetry-breaking requirements. To overcome these obstacles, we design a strategy to exploit the interfacial Rashba effect (IRE) of lead-free 2D HOIP (C6H5CH2CH2NH3)2CuCl4 (PEA-CuCl), manifesting as an efficient spin generator at room temperature. IRE of PEA-CuCl originates from the large orbital hybridization at the interface between PEA-CuCl and adjacent ferromagnetic layers. Spin-torque ferromagnetic resonance measurements further quantify a large Rashba effective field of 14.04 Oe per 10^11 A m-2, surpassing those of lead-based HOIP and traditional all-inorganic heterojunctions with noble metals. Our lead-free 2D HOIP PEA-CuCl, which harnesses large IRE for spin generation, is efficient, nontoxic, and economic, offering huge promise for future flexible and wearable spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Tunable Quantum Anomalous Hall Effect via Crystal Order in Spin-Splitting Antiferromagnets

Quantum anomalous Hall (QAH) effect provides dissipationless chiral channels for spin transport, expected as an outstanding candidate in future low-power quantum computation. The spin-splitting band structure is vital for obtaining QAH effect in topological systems, with ferromagnetism indispensable to manipulate the Chern number. Herein, we challenge this wisdom by proposing tunable QAH effect in spin-splitting antiferromagnets with zero magnetization. Since the spin splitting of these unique magnets originates from the alternate crystal environment, the Chern number can be modulated not only by the conventional magnetic order, but also by the crystal order, opening an additional dimension for tuning QAH effect. Our concept is illustrated based on two-dimensional (2D) MnBi2Te4 (MBT) with even septuple layers (SLs), a typical axion insulator with fully magnetic compensation. By interlayer rotation and translation operations, sublattices of MBT with opposite magnetizations are no longer connected by inversion or mirror symmetries, leading to the transition to the QAH insulator. The flexible stacking of 2D materials enables the reversible Chern number by crystal design. Our work fundamentally reveals the crystal-order-dependent QAH effect in spin-splitting antiferromagnets, which would advance QAH effect-based devices towards high controllability, integration density and operation speed.

cond-mat.mes-hall↗