相关论文: Attention is all you need to solve chiral supercon…

Gapless materials in electronic contact with superconductors acquire proximity-induced superconductivity in a region near the interface. Numerous proposals build on this addition of electron pairing to originally non-superconducting systems…

We investigate a mechanism to produce superconductivity by strong purely repulsive interactions for flat dispersion $\varepsilon \sim k^4$, without using pairing instability in Fermi-liquid. The resulting superconductors break both…

We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer), which can be used as an approximation (or Ansatz) for solving the many-electron Schr\"odinger equation, the fundamental equation…

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches. These architectures…

Chiral $p$-wave superfluids are fascinating topological quantum states of matter that have been found in the liquid $^3$He-A phase and arguably in the electronic Sr$_2$RuO$_4$ superconductor. They are shown fundamentally related to the…

Point contacts provide simple connections between macroscopic particle reservoirs. In electric circuits, strong links between metals, semiconductors or superconductors have applications for fundamental condensed-matter physics as well as…

We present an attention-based foundation model architecture for learning and predicting quantum states across Hamiltonian parameters, system sizes, and physical systems. Using only basis configurations and physical parameters as inputs, our…

Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterization. However, this necessitates development of methods for…

The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention…

The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains. Most such models use multi-head self-attention which is appealing for the ability to attend to…

We propose a novel attention model that can accurately attends to target objects of various scales and shapes in images. The model is trained to gradually suppress irrelevant regions in an input image via a progressive attentive process…

We introduce a new and general mechanism for superconductivity in Fermi systems with strong repulsive interaction. Because kinetic terms are small compared to the bare repulsion, the dynamic of charge carriers is constrained by the the…

This work present a new class of variational wave functions for fermi systems in any dimension. These wave functions introduce correlations between Cooper pairs in different momentum states and the relevant correlations can be computed…

The quasiparticles in the normal state of cuprate superconductors have been shown to behave universally as a 3-dimensional Fermi liquid. Because of interactions and the presence of the Fermi surfaces (or Fermi energies), the quasiparticle…

This paper proposes a novel Attention-based Multi-Reference Super-resolution network (AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar texture from multiple reference images to the super-resolution…

Local features at neighboring spatial positions in feature maps have high correlation since their receptive fields are often overlapped. Self-attention usually uses the weighted sum (or other functions) with internal elements of each local…

Ultra-cold Fermi gases display diverse quantum mechanical properties, including the transition from a fermionic superfluid BCS state to a bosonic superfluid BEC state, which can be probed experimentally with high precision. However, the…

Attention-based graph neural networks have made great progress in feature matching learning. However, insight of how attention mechanism works for feature matching is lacked in the literature. In this paper, we rethink cross- and…

We consider superconductivity in a system with $N$ Fermi surfaces, including intraband and interband effective electron-electron interactions. The effective interaction is described by an $N \times N$ matrix whose elements are assumed to be…

We establish the universal approximation capability of single-layer, single-head self- and cross-attention mechanisms with minimal attached structures. Our key insight is to interpret single-head attention as an input domain-partition…