semantic_scholar / 2025

Attention is all you need to solve chiral superconductivity

Chun-Tse Li, T. Ong, Max Geier, Hsin Lin, Liang Fu

Foundation ModelsGraph LearningInformation RetrievalPopular and Landmark Papers

Recent advances on neural quantum states have shown that correlations between quantum particles can be efficiently captured by {\it attention} -- a foundation of modern neural architectures that enables neural networks to learn the relation between objects. In this work, we show that a general-purpose self-attention Fermi neural network is able to find chiral $p_x \pm i p_y$ superconductivity in an attractive Fermi gas by energy minimization, {\it without prior knowledge or bias towards pairing}. The superconducting state is identified from the optimized wavefunction by measuring various physical observables: the pair binding energy, the total angular momentum of the ground state, and off-diagonal long-range order in the two-body reduced density matrix. Our work paves the way for AI-driven discovery of unconventional and topological superconductivity in strongly correlated quantum materials.

12 citations0 influential

Full paper

Read the original paper

Open PDF Source page

Learning resources

arXiv PDFPDF arXiv abstract pagearXiv Google Scholar referencesGoogle Scholar Papers with Code searchPapers with Code YouTube explanationsYouTube

Reading state

Discuss in ChatGPT

Uses your own ChatGPT account. The paper context is copied into a tutor prompt before ChatGPT opens.

Preview prompt

You are my AI/ML research paper instructor. I want to deeply understand the paper below.

First, teach it in layers:
1. One-paragraph intuition.
2. Problem statement and why it mattered.
3. Key method, architecture, or algorithm.
4. Important equations or mechanisms, explained intuitively.
5. Experiments and evidence.
6. Limitations, assumptions, and failure modes.
7. How this paper influenced later AI/ML/Deep Learning/GenAI work.
8. A 30-minute study plan with checkpoints.
9. Quiz me with 5 questions and wait for my answers.

When something is not available in the attached context, say what is missing and infer carefully.

### Paper attached as context
Title: Attention is all you need to solve chiral superconductivity
Authors: Chun-Tse Li, T. Ong, Max Geier, Hsin Lin, Liang Fu
Year: 2025
Venue:
Categories: Foundation Models, Graph Learning, Information Retrieval, Popular and Landmark Papers
Citations: 12
Paper URL: https://arxiv.org/abs/2509.03683v1
Open PDF: https://arxiv.org/pdf/2509.03683v1

Abstract:
Recent advances on neural quantum states have shown that correlations between quantum particles can be efficiently captured by {\it attention} -- a foundation of modern neural architectures that enables neural networks to learn the relation between objects. In this work, we show that a general-purpose self-attention Fermi neural network is able to find chiral $p_x \pm i p_y$ superconductivity in an attractive Fermi gas by energy minimization, {\it without prior knowledge or bias towards pairing}. The superconducting state is identified from the optimized wavefunction by measuring various physical observables: the pair binding energy, the total angular momentum of the ground state, and off-diagonal long-range order in the two-body reduced density matrix. Our work paves the way for AI-driven discovery of unconventional and topological superconductivity in strongly correlated quantum materials.

Learning resources:
- PDF: arXiv PDF (https://arxiv.org/pdf/2509.03683v1)
- arXiv: arXiv abstract page (https://arxiv.org/abs/2509.03683v1)
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Attention%20is%20all%20you%20need%20to%20solve%20chiral%20superconductivity)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Attention%20is%20all%20you%20need%20to%20solve%20chiral%20superconductivity)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Attention%20is%20all%20you%20need%20to%20solve%20chiral%20superconductivity+paper+explained)