arXiv subjects

Dongheng Qian

Publications and source records attributed to Dongheng Qian.

13 recordsLinked to original sources

arXiv2026

Machine learning reveals common features of unconventional superconductors with high transition temperatures

Superconductors with high critical temperatures that emerges beyond the phonon-mediated regime are usually considered unconventional in nature, yet unlike conventional superconductors, no broadly applicable predictive theory currently guides their discovery. Here, we use interpretable machine learning to uncover a common materials-space signature of high-$T_{\mathrm{c}}$ unconventional superconductors and develop a data-driven strategy for materials discovery. We construct a unified feature representation for each material by integrating compositional statistics, structural information, and latent representations from trained property-prediction models, followed by structure-aware filtering of an experimentally established superconducting dataset. Without using transition-temperature information, unsupervised analysis shows that cuprate and iron-based superconductors occupy a common region of materials space, characterized primarily by large electronegativity deviation and intermediate mean valence-electron number. A supervised $T_{\text{c}}$ model independently identifies the same descriptors as dominant features, providing complementary evidence for their relevance. Using this empirical materials-space prior together with the $T_{\text{c}}$ model, we prioritize candidate materials, recover recently discovered nickelate superconductors, and identify chemically distinct candidates for future investigation.

cond-mat.supr-con↗

arXiv2026

Multi-agent discovery of practical quantum LDPC codes

Quantum low-density parity-check (qLDPC) codes can encode multiple logical qubits using sparse parity checks, yet searching for useful finite-length instances remains a challenging design problem because code performance must be optimized while satisfying practical constraints. Motivated by recent advances in artificial-intelligence agents for scientific discovery, we develop a multi-agent framework for discovering practical qLDPC codes. The framework combines specialist proposal and review, persistent scientific memory, long-horizon evolution of executable programs, and deterministic construction and evaluation within a closed-loop search. These programs instantiate coset-orbit balanced-product codes, providing a search space that includes bicycle and lifted-product constructions as well as non-normal subgroup actions. To incorporate practical constraints, we restrict the search to binary CSS codes with block length $n\leq400$ and overall weight $w\leq10$. Within this regime, the framework discovers codes with leading or competitive rate--distance performance in every weight class considered, with representative instances including $[[288,16,18]]$ at $w=7$, $[[288,18,18]]$ at $w=9$, and $[[234,28,18]]$ at $w=10$. The search also uncovers structurally distinct, high-performing constructions, including a $[[336,12,\leq24]]$ candidate and a $[[368,18,16]]$ code, both of which are genuine balanced-product constructions with non-normal subgroup actions. When evaluated under code-capacity depolarizing noise using a common BP-OSD decoding protocol, the discovered codes also exhibit low logical failure rates. Together, these results provide hardware-relevant finite-length candidates for further experimental evaluation and show how structured agentic search can contribute to scientific discovery.

quant-ph↗

arXiv2026

Data-Driven Learnability Transition of Measurement-Induced Entanglement

Measurement-induced entanglement (MIE) captures how local measurements generate long-range quantum correlations and drive dynamical phase transitions in many-body systems. Yet estimating MIE experimentally remains challenging: direct evaluation requires extensive post-selection over measurement outcomes, raising the question of whether MIE is accessible with only polynomial resources. We address this challenge by reframing MIE detection as a data-driven learning problem that assumes no prior knowledge of state preparation. Using measurement records alone, we train a neural network in a self-supervised manner to predict the uncertainty metric for MIE--the gap between upper and lower bounds of the average post-measurement bipartite entanglement. Applied to random circuits with one-dimensional all-to-all connectivity, our method reveals a learnability transition with increasing circuit depth: below a threshold the MIE can be effectively learned with resources that grow only polynomially with system size, whereas above it the required resources grow exponentially. This computational phase transition coincides with the breakdown of efficient classical simulation of the underlying quantum state. We further observe signatures of this transition on current noisy quantum devices. These results highlight the power of data-driven approaches for learning MIE and delineate the practical limits of its classical learnability.

quant-ph↗

arXiv2026

Enhancing Neural-Network Variational Monte Carlo through Basis Transformation

Neural-network variational Monte Carlo (NNVMC) has emerged as a powerful tool for solving quantum many-body problems, yet systematic pathways for improving its accuracy remain largely heuristic. Here, we introduce a physically motivated basis transformation for NNVMC that enhances variational expressivity without increasing the complexity of the neural-network ansatz itself. By formulating the many-body wave function in a Gaussian basis, we introduce a single learnable locality parameter, $α$, that reshapes the target ground state into a more learnable representation. This approach introduces minimal computational overhead and can be readily combined with existing neural-network architectures. Using the three-dimensional homogeneous electron gas as a benchmark, we show that the optimized basis transformation consistently lowers the variational energy for both FermiNet and message-passing neural-network architectures. Notably, for the latter, it enables a more precise determination of the Fermi liquid to Wigner crystal phase transition. More broadly, our results highlight basis transformation as a new route to improving NNVMC in continuous space, showing that accuracy can be enhanced not only by refining the ansatz but also by making the target ground state easier to represent.

cond-mat.str-el↗

arXiv2026

Revealing Superconducting Chiral Edge Modes via Resistance Distributions

Inducing superconducting correlations in quantum anomalous Hall (QAH) states offers a promising route to realize topological superconductivity with chiral Majorana edge modes. However, the definitive identification of these modes is challenging. Here we propose detecting superconducting chiral edge modes via the probability distribution of the resistance, or equivalently the charge transmission of QAH-superconductor heterojunctions. Remarkably, the distribution for coherent edge exhibits distinct characteristics for different topological superconducting phases in sufficiently long junctions, and this difference remains robust against weak decoherence. These findings provide insights into transport phenomena beyond the clean limit and highlight the resistance distribution as a compelling signature for distinguishing topological superconducting phases.

cond-mat.mes-hall↗

arXiv2025

Coherent Information Phase Transition in a Noisy Quantum Circuit

Coherent information quantifies the transmittable quantum information through a channel and is directly linked to the channel's quantum capacity. In a monitored quantum circuit, regarded as a quantum channel, extensive and positive coherent information is sustained at low measurement rates, protected by the scrambling dynamics. However, noise suppresses coherent information, driving it to zero or negative values. Here, we show that incorporating quantum-enhanced operations facilitates reliable quantum information transmission even in the presence of noise, as evidenced by a phase transition in coherent information from a recoverable phase with positive values to an irrecoverable phase with negative values. We provide both analytical understanding and numerical evidence demonstrating this transition, which is modulated by the relative frequencies of noise and quantum-enhanced operations. Additionally, we propose a resource-efficient protocol to characterize this phase transition in experiments, effectively avoiding post-selection by utilizing every run of the quantum circuit. This approach bridges the gap between theoretical insights and practical implementation, making the phase transition feasible to demonstrate on realistic noisy intermediate-scale quantum devices.

quant-ph↗

arXiv2025

Intrinsic Quantum Mpemba Effect in Markovian Systems and Quantum Circuits

The quantum Mpemba effect (QME) describes the counterintuitive phenomenon in which a system farther from equilibrium reaches steady state faster than one closer to equilibrium. However, ambiguity in defining a suitable distance measure between quantum states has led to varied interpretations across different contexts. Here we propose the intrinsic quantum Mpemba effect (IQME), defined using the trajectory length traced by the quantum state as a more appropriate measure of distance--distinct from previous trajectory-independent metrics. By treating quantum states as points in a Riemannian space defined by statistical distance, the trajectory length emerges as a more natural and accurate characterization of the counterintuitive dynamics, drawing an analogy to the classical Brachistochrone problem. We demonstrate the existence of IQME in Markovian systems and extend its definition to quantum circuits, thereby establishing a unified framework applicable to both open and closed systems. Notably, we observe an IQME in a $U(1)$-symmetric circuit, offering new insights into the rates of quantum thermalization for different initial states. This work deepens our understanding of quantum state evolution and lays the foundation for accurately capturing novel quantum dynamical behaviour.

quant-ph↗

arXiv2025

Quantum Non-Local Nonstabilizerness

Quantum entanglement and quantum nonstabilizerness are fundamental resources that characterize distinct aspects of a quantum state: entanglement reflects non-local correlations, while nonstabilizerness quantifies the deviation from stabilizer states. A quantum state becomes a valuable resource for applications like universal quantum computation only when both quantities are present. Here, we propose that quantum non-local nonstabilizerness (NN) serves as an effective measure of this combined resource, incorporating both entanglement and nonstabilizerness. We demonstrate that NN can be precisely computed for two-qubit pure states, where it is directly related to the entanglement spectrum. We then extend the definition of NN to mixed states and explore its presence in many-body quantum systems, revealing that the two-point NN decays according to a power law in critical states. Furthermore, we explore measurement-induced NN and uncover an intriguing phenomenon termed "nonstabilizerness swapping", analogous to entanglement swapping, wherein post-measurement NN decays more slowly than any pre-measurement correlations. Our results thus represent a pivotal step towards accurately quantifying the "quantumness" of a state and reveal the potential for manipulating this resource through measurements.

quant-ph↗

arXiv2025

Design Topological Materials by Reinforcement Fine-Tuned Generative Model

Topological insulators (TIs) and topological crystalline insulators (TCIs) are materials with unconventional electronic properties, making their discovery highly valuable for practical applications. However, such materials, particularly those with a full band gap, remain scarce. Given the limitations of traditional approaches that scan known materials for candidates, we focus on the generation of new topological materials through a generative model. Specifically, we apply reinforcement fine-tuning (ReFT) to a pre-trained generative model, thereby aligning the model's objectives with our material design goals. We demonstrate that ReFT is effective in enhancing the model's ability to generate TIs and TCIs, with minimal compromise on the stability of the generated materials. Using the fine-tuned model, we successfully identify a large number of new topological materials, with Ge$_2$Bi$_2$O$_6$ serving as a representative example--a TI with a full band gap of 0.26 eV, ranking among the largest known in this category.

cond-mat.mtrl-sci↗

arXiv2024

Protect Measurement-Induced Phase Transition from Noise

Scrambling dynamics induced by random unitary gates can protect information from low-rate measurements, which underpins the phenomenon known as the measurement-induced phase transition (MIPT). However, typical decoherence noises disrupts the volume law phase, complicating the observation of MIPT on noisy intermediate-scale quantum devices. Here, we demonstrate that incorporating quantum-enhanced operations can effectively protect MIPT from environmental noise, thereby enabling its detection in experiment. The transition is characterized by the conditional entanglement entropy (CEE), which is associated with a statistical mechanics model wherein noise and quantum-enhanced operations act as competing external random fields. When the net external field is zero, a ferromagnetic-paramagnetic phase transition is expected, resulting in the MIPT. This zero-field condition also ensures an average apparatus-environment symmetry, making CEE a valid probe of entanglement and establishing the transition as a genuine entanglement phase transition. Additionally, we provide numerical results demonstrate the MIPT in a (2+1)-dimensional quantum circuit under dephasing noise. We also propose a method to estimate the noise rate, enabling the zero-field condition to be achieved experimentally and ensuring the feasibility of our protocol. Our result serves as a concrete example of the power of quantum enhancement in combating noise.

quant-ph↗

arXiv2024

Predicting Many Crystal Properties via an Adaptive Transformer-based Framework

Machine learning has revolutionized many fields, including materials science. However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91\% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science.

cond-mat.mtrl-sci↗

arXiv2023

Steering-induced phase transition in measurement-only quantum circuits

Competing measurements alone can give rise to distinct phases characterized by entanglement entropy$\unicode{x2013}$such as the volume law phase, symmetry-breaking (SB) phase, and symmetry-protected topological (SPT) phase$\unicode{x2013}$that can only be discerned through quantum trajectories, making them challenging to observe experimentally. In another burgeoning area of research, recent studies have demonstrated that steering can give rise to additional phases within quantum circuits. In this work, we show that new phases can appear in measurement-only quantum circuit with steering. Unlike conventional steering methods that rely solely on local information, the steering scheme we introduce requires the circuit's structure as an additional input. These steering induced phases are termed as "informative" phases. They are distinguished by the intrinsic dimension of the bitstrings measured in each circuit run, making them substantially easier to detect in experimental setups. We explicitly show this phase transition by numerical simulation in three circuit models that are previously well-studied: projective transverse field Ising model, lattice gauge-Higgs model and XZZX model. When the informative phase coincides with the SB phase, our steering mechanism effectively serves as a "pre-selection" routine, making the SB phase more experimentally accessible. Additionally, an intermediate phase may manifest, where a discrepancy arises between the quantum information captured by entanglement entropy and the classical information conveyed by bitstrings. Our findings demonstrate that steering not only adds theoretical richness but also offers practical advantages in the study of measurement-only quantum circuits.

quant-ph↗

arXiv2021

Dissipative Edge Transport in Disordered Axion Insulator Films

We investigate the role of disorder in the edge transport of axion insulator films. We predict by first-principles calculations that even-number-layer MnBi$_2$Te$_4$ have gapped helical edge states. The random potential will dramatically modify the edge spectral function to become gapless. However, such gapless helical state here is fundamentally different from that in quantum spin Hall insulator or topological Anderson insulator. We further study the edge transport in this system by Landauer-Büttiker formalism, and find such gapless edge state is dissipative and not immune to backscattering, which would explain the dissipative nonlocal transport in the axion insulator state observed in six septuple layer MnBi$_2$Te$_4$ experimentally. Several transport experiments are proposed to verify our theory on the dissipative helical edge channels. In particular, the longitudinal resistance can be greatly reduced by adding an extra floating probe even if it is not used. These results will facilitate the observsation of long-sought topological magnetoelectric effect in axion insulators.

cond-mat.mes-hall↗