arXiv subjects

Fang Zhang

Publications and source records attributed to Fang Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Quantum Design Automation: Foundations, Challenges, and the Road Ahead

Quantum computing is transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.

quant-ph↗

arXiv2025

Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation. Neural network decoders like AlphaQubit have demonstrated significant potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for sliding-window decoding. While our network still outputs a single bit per window, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we resolve the throughput limitation that previously prohibited the application of AlphaQubit-type decoders in fault-tolerant quantum computation.

quant-ph↗

arXiv2025

LATTE: A Decoding Architecture for Quantum Computing with Temporal and Spatial Scalability

Quantum error correction allows inherently noisy quantum devices to emulate an ideal quantum computer with reasonable resource overhead. As a crucial component, decoding architectures have received significant attention recently. In this paper, we introduce LATTE, a FPGA-CPU hybrid decoding architecture aiming to address the key requirements of scaling up in lattice surgery quantum computation -- Latency, Accuracy, Throughput and Transmission Bandwidth, in an Eclectic manner. LATTE follows a hierarchical design: (1) A fully streaming and asynchronous block decoding system on CPU to enable parallelization both temporally and spatially. (2) A super-light yet accurate neural local decoding unit integrated with quantum control hardware on FPGA, which remains \emph{transparent} to the block decoding system, effectively reducing transmission bandwidth and accelerating the decoding process. LATTE delivers accuracy on par with the base decoder while achieving real-time decoding throughput and significantly reducing both bandwidth requirements and computational resources, enabling a level of scalability far beyond previous approaches. Under circuit-level noise $p=0.001$, LATTE achieves over $\mathbf{90\%}$ reduction in transmission bandwidth and a $\mathbf{6.4\times}$ speedup on average in single-block decoding. In the \emph{streaming decoding} scenario: (1) LATTE achieves constant and low latency ($\mathbf{16\times}$-$\mathbf{20\times}$ speedup over existing streaming decoding implementations) in arbitrarily long quantum memory experiments, with near-optimal resources -- merely $\mathbf{2}$ threads are sufficient for decoding the surface code with distance up to $17$. (2) LATTE minimizes latency in multi-patch measurement experiments through highly parallelized decoding operations. These combined efforts ensure sufficient scalability for large-scale fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Louvre: Relaxing Hardware Requirements of Quantum LDPC Codes by Routing with Expanded Quantum Instruction Set

Generalized bicycle codes (GB codes) represent a promising family of quantum low-density parity-check codes, characterized by high code rates and relatively local qubit connectivity. A subclass of the GB code called bivariate bicycle codes (BB codes) has garnered significant interest due to their compatibility with two-layer connectivity architectures on superconducting quantum processors. However, one key limitation of BB codes is their high qubit connectivity degree requirements (degree 6), which exacerbates the noise susceptibility of the system. Building on the recent progress in implementing multiple two-qubit gates on a single chip, this work introduces Louvre -- a routing-based framework designed to reduce qubit connectivity requirements in GB codes. Specifically, Louvre-7 achieves degree reduction while preserving the depth of the syndrome extraction circuit, whereas Louvre-8 further minimizes the connectivity by slightly increasing the circuit depth. When applied to BB codes, these two schemes could reduce the average degree to 4.5 and 4, respectively. Crucially, Louvre eliminates some of the long-range, error-prone connections, which is a distinct advantage over prior approaches. Numerical simulations demonstrate that Louvre-7 has an indistinguishable logical error rate as the standard syndrome extraction circuits of GB codes, while Louvre-8 only incurs a slight error rate penalty. Furthermore, by reordering some of the gates in the circuit, we can reduce the coupler length without degrading the performance. Though most of our analysis focuses on GB codes defined on periodic boundary conditions, we further discuss the adaptability of Louvre to open-boundary lattices and defect-containing grids, underscoring its broader applicability in practical quantum error correction architectures.

quant-ph↗

arXiv2025

$L^p$-estimates for the wave equation with critical magnetic field in higher dimensions

In this paper, we study the $L^{p}$-estimates for the solution to the wave equation with a scaling-critical magnetic potential in Euclidean $R^N$ with $N\geq3$. Inspired by the work of \cite{L}, we show that the operators $(I+\mathcal{L}_{\mathbf{A}})^{-γ}e^{it\sqrt{\mathcal{L}_{\mathbf{A}}}}$ is bounded in $L^{p}(\mathbb{R}^{N})$ for $1 |1/p-1/2|$ and $t>0$, where $\mathcal{L}_{\mathbf{A}}$ is a magnetic Schrödinger operator. In particular, we derive the $L^{p}$-bounds for the sine wave propagator $\sin(t\sqrt{\mathcal{L}_{\mathbf{A}}})\mathcal{L}^{-\frac12}_{\mathbf{A}}$. The key ingredient is the $L^p\rightarrow L^p$ boundedness of the analytic operator family $f_{w,t}(\mathcal{L}_{\mathbf{A}})$.

math.AP↗

arXiv2025

Strichartz estimates for Critical magnetic Schrödinger operators on flat Euclidean cones

In this paper, we study Schrödinger operator $\mathcal{H}_{\mathbf{A}}$ perturbed by critical magnetic potentials on the 2D flat cone $Σ= C(\mathbb{S}_ρ^1) = (0, \infty) \times \mathbb{S}_ρ^1$, which is a product cone over the circle $\mathbb{S}_ρ^1 = \mathbb{R}/2πρ\mathbb{Z}$ with radius $ρ> 0$, and equipped with the metric $g = dr^2 + r^2 dθ^2$. The goal of this work is to establish Strichartz estimates for $\mathcal{H}_{\mathbf{A}}$ in this setting. A key aspect of our approach is the construction of the Schwartz kernel of the resolvent and the spectral measure for Schrödinger operator on the flat Euclidean cone $(Σ, g)$. The results presented here generalize previous work in \cite{Ford, BFM, FZZ, Zhang1}.

math.AP↗

arXiv2025

$L^p$-estimates for the wave equation with critical magnetic potential on conical manifolds

In this paper, we consider a class of conical singular spaces $Σ=(0,\infty)_r\times Y$ equipped with the metric $g=\mathrm{d}r^2+r^2h$, where the cross section $Y$ is a compact $(n-1)$-dimensional closed Riemannian manifold $(Y,h)$ without boundary. In this context, we aim to show that the sine wave propagator $\sin\left(t\sqrt{\mathcal{L}_{\mathbf{A}}}\right)/\sqrt{\mathcal{L}_{\mathbf{A}}}$ is bounded in $L^{p}(Σ)$, where $\mathcal{L}_{\mathbf{A}}$ is a magnetic Schrödinger operator with a scaling-critical magnetic potential on metric cone $Σ$. Our main result is the generalization of the result in \cite{L}. The novel ingredient is the construction of Hadamard parametrix for $\cos\left(t\sqrt{\mathcal{L}_{\bf A}}\right)$ on $Σ$.

math.AP↗

arXiv2025

Hybrid neural network method of a multilayer perceptron and autoencoder for the α-particle preformation factor in α-decay theory

The preformation factor quantifies the probability of α particles preforming on the surface of the parent nucleus in decay theory and is closely related to the study of α clustering structure. In this work, a multilayer perceptron and autoencoder (MLP + AE) hybrid neural network method is introduced to extract preformation factors within the generalized liquid drop model and experimental data. A K-fold cross validation method is also adopted. The accuracy of the preformation factor calculated by this improved neural network is comparable to the results of the empirical formula. MLP + AE can effectively capture the linear relationship between the logarithm of the preformation factor and the square root of the ratio of the decay energy, further verifying that Geiger-Nuttall law can deal with preformation factor. The extracted preformation probability of isotope and isotone chains show different trends near the magic number, and in addition, an odd-even staggering effect appears. This means that the preformation factors are affected by closed shells and unpaired nucleons. Therefore the preformation factors can provide nuclear structure information. Furthermore, for 41 new nuclides, the half-lives introduced with the preformation factors reproduce the experimental values as expected. Finally, the preformation factors and α-decay half-lives of Z = 119 and 120 superheavy nuclei are predicted.

nucl-th↗

arXiv2025

Ab initio study of exciton insulator phase: Emergent $\textit{p}$-wave spin textures from spontaneous excitonic condensation

An excitonic insulator$^{1,2}$ (EI) is a correlated many-body state of electron-hole pairs, potentially leading to high-temperature condensate and superfluidity$^{3-7}$. Despite ever-growing experiments suggesting possible EI states in various materials, direct proofs remain elusive and debated. Here we address the problem by introducing an ab initio methodology, enabling the parameter-free determination of electron-hole pairing order parameter and single-particle excitations within a Bardeen-Cooper-Schrieffer (BCS)-type formalism. Our calculations on monolayer 1T'-MoS$_{2}$$^{8,9}$ reveals that it is an unconventional EI with a transition temperature ~900K, breaking spontaneously the crystal's inversion, rotation, and mirror symmetries, while maintaining odd parity and unitarity. We identify several telltale spectroscopic signatures emergent in this EI phase that distinguish it from the band insulator (BI) phase, exemplified with a giant $\textbf{k}$-dependent $\textit{p}$-wave spin texture.

cond-mat.mtrl-sci↗

arXiv2025

$L^p$-estimates for the 2D wave equation in the scaling-critical magnetic field

In this paper, we study the $L^{p}$-estimates for the solution to the $2\mathrm{D}$-wave equation with a scaling-critical magnetic potential. Inspired by the work of \cite{FZZ}, we show that the operators $(I+\mathcal{L}_{\mathbf{A}})^{-γ}e^{it\sqrt{\mathcal{L}_{\mathbf{A}}}}$ is bounded in $L^{p}(\mathbb{R}^{2})$ for $1 |1/p-1/2|$ and $t>0$, where $\mathcal{L}_{\mathbf{A}}$ is a magnetic Schrödinger operator. In particular, we derive the $L^{p}$-bounds for the sine wave propagator $\sin(t\sqrt{\mathcal{L}_{\mathbf{A}}})\mathcal{L}^{-\frac12}_{\mathbf{A}}$. The key ingredients are the construction of the kernel function and the proof of the pointwise estimate for an analytic operator family $f_{w,t}(\mathcal{L}_{\mathbf{A}})$.

math.AP↗

arXiv2025

Deep Learning based Quasi-consciousness Training for Robot Intelligent Model

This paper explores a deep learning based robot intelligent model that renders robots learn and reason for complex tasks. First, by constructing a network of environmental factor matrix to stimulate the learning process of the robot intelligent model, the model parameters must be subjected to coarse & fine tuning to optimize the loss function for minimizing the loss score, meanwhile robot intelligent model can fuse all previously known concepts together to represent things never experienced before, which need robot intelligent model can be generalized extensively. Secondly, in order to progressively develop a robot intelligent model with primary consciousness, every robot must be subjected to at least 1~3 years of special school for training anthropomorphic behaviour patterns to understand and process complex environmental information and make rational decisions. This work explores and delivers the potential application of deep learning-based quasi-consciousness training in the field of robot intelligent model.

cs.RO↗

arXiv2025

A real-time battle situation intelligent awareness system based on Meta-learning & RNN

In modern warfare, real-time and accurate battle situation analysis is crucial for making strategic and tactical decisions. The proposed real-time battle situation intelligent awareness system (BSIAS) aims at meta-learning analysis and stepwise RNN (recurrent neural network) modeling, where the former carries out the basic processing and analysis of battlefield data, which includes multi-steps such as data cleansing, data fusion, data mining and continuously updates, and the latter optimizes the battlefield modeling by stepwise capturing the temporal dependencies of data set. BSIAS can predict the possible movement from any side of the fence and attack routes by taking a simulated battle as an example, which can be an intelligent support platform for commanders to make scientific decisions during wartime. This work delivers the potential application of integrated BSIAS in the field of battlefield command & analysis engineering.

cs.LG↗

arXiv2024

Decay estimates for Nonlinear Schrödinger equation with the inverse-square potential

In this paper, we study the dispersive decay estimates for solution to the $3\mathrm{D}$ energy-critical nonlinear Schrödinger equation with an inverse-square operator $\mathcal{L}_a$ where the operator is denoted by $\mathcal{L}_{a}:=-Δ+\frac{a}{|x|^2}$ with the constant $a\geq0$. Inspired by the work of \cite{KMVZZ1,K}, we first establish that the solutions exhibit $\dot{H}^1(\R^3)$ uniform regularity, derive the Lorentz-Strichartz estimates, and then obtain the desired decay estimates using the bootstrap argument. The key ingredients of our approach include the equivalence of Sobolev norms and the fractional product rule.

math.AP↗

arXiv2024

Emerging quantum critical phase in a cluster spin-glass

Magnetic frustration has been recognized as pivotal to investigating new phases of matter in correlation-driven Kondo breakdown quantum phase transitions that are not clearly associated with broken symmetry. The nature of these new phases, however, remains underexplored. Here, we report quantum criticalities emerging from a cluster spin-glass in the heavy-fermion metal TiFe$_x$Cu$_{2x-1}$Sb, where frustration originates from intrinsic disorder. Specific heat and magnetic Grüneisen parameter measurements under varying magnetic fields exhibit quantum critical scaling, indicating a quantum critical point near 0.13 Tesla. As the magnetic field increases, the cluster spin-glass phase is progressively suppressed. Upon crossing the quantum critical point, resistivity and Hall effect measurements reveal enhanced screening of local moments and an expanding Fermi surface, consistent with the Kondo breakdown scenario.

cond-mat.str-el↗

arXiv2024

An Instance-based Plus Ensemble Learning Method for Classification of Scientific Papers

The exponential growth of scientific publications in recent years has posed a significant challenge in effective and efficient categorization. This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers into relevant research fields. Working with a classification system with a group of research fields, first a number of typical seed papers are allocated to each of the fields manually. Then for each paper that needs to be classified, we compare it with all the seed papers in every field. Contents and citations are considered separately. An ensemble-based method is then employed to make the final decision. Experimenting with the datasets from DBLP, our experimental results demonstrate that the proposed classification method is effective and efficient in categorizing papers into various research areas. We also find that both content and citation features are useful for the classification of scientific papers.

cs.DL↗

arXiv2024

Empowering Diffusion Models on the Embedding Space for Text Generation

Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space. In this paper, we conduct systematic studies of the optimization challenges encountered with both the embedding space and the denoising model, which have not been carefully explored. Firstly, the data distribution is learnable for embeddings, which may lead to the collapse of the embedding space and unstable training. To alleviate this problem, we propose a new objective called the anchor loss which is more efficient than previous methods. Secondly, we find the noise levels of conventional schedules are insufficient for training a desirable denoising model while introducing varying degrees of degeneration in consequence. To address this challenge, we propose a novel framework called noise rescaling. Based on the above analysis, we propose Difformer, an embedding diffusion model based on Transformer. Experiments on varieties of seminal text generation tasks show the effectiveness of the proposed methods and the superiority of Difformer over previous state-of-the-art embedding diffusion baselines.

cs.CL↗

arXiv2023

Decay estimates for one Aharonov-Bohm solenoid in a uniform magnetic field I: Schrödinger equation

This is the first of a series of papers in which we investigate the decay estimates for dispersive equations with Aharonov-Bohm solenoids in a uniform magnetic field. In this first starting paper, we prove the local-in-time dispersive estimates and Strichartz estimates for Schrödinger equation with one Aharonov-Bohm solenoid in a uniform magnetic field. The key ingredient is the construction of Schrödinger propagator, we provide two methods to construct the propagator. The first one is combined the strategies of \cite{FFFP1} and \cite{GYZZ22, FZZ22}, and the second one is based on the Schulman-Sunada formula in sprit of \cite{stov, stov1} in which the heat kernel has been studied. In future papers, we will continue investigating this quantum model for wave with one or multiple Aharonov-Bohm solenoids in a uniform magnetic field.

math.AP↗

arXiv2023

Decay estimates for one Aharonov-Bohm solenoid in a uniform magnetic field II: wave equation

This is the second of a series of papers in which we investigate the decay estimates for dispersive equations with Aharonov-Bohm solenoids in a uniform magnetic field. In our first starting paper \cite{WZZ}, we have studied the Strichartz estimates for Schrödinger equation with one Aharonov-Bohm solenoid in a uniform magnetic field. The wave equation in this setting becomes more delicate since a difficulty is raised from the square root of the eigenvalue of the Schrödinger operator $H_{α, B_0}$ so that we cannot directly construct the half-wave propagator. An independent interesting result concerning the Gaussian upper bounds of the heat kernel is proved by using two different methods. The first one is based on establishing Davies-Gaffney inequality in this setting and the second one is straightforward to construct the heat kernel (which efficiently captures the magnetic effects) based on the Schulman-Sunada formula. As byproducts, we prove optimal bounds for the heat kernel and show the Bernstein inequality and the square function inequality for Schrödinger operator with one Aharonov-Bohm solenoid in a uniform magnetic field.

math.AP↗