[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84566-en":3,"doc-seo-84566-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84566,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Holographic Quantum Transformer Generalist Neuro-Symbolic Architecture for Solving Frustrated Systems via Generative Attention","Simulating two-dimensional frustrated quantum matter is difficult due to the sign problem and exponential growth of the Hilbert space. The work proposes the Holographic Quantum Transformer (HQT), a physics-inspired generative model using global self-attention to capture non-local entanglement structure. Results on the square-lattice J1−J2 Heisenberg model show accurate energies near the quantum critical point and interpretable attention maps recovering the interaction geometry. A key “Holographic Transfer” enables zero-shot size extrapolation from 8×8 to 10×10 without retraining, with energies consistent with state-of-the-art variational methods.","Holographic Quantum Transformer: A Generalist Neuro-Symbolic Architecture for Solving Frustrated Systems via  \nGenerative Attention  \narXiv :2607 .00398v1 [ cond-mat .str-el ] 1 Jul 2026  \nXingran Guo  \nNational University of Defense Technology Changsha, China [gxrnudt@nudt.edu.cn](gxrnudt@nudt.edu.cn)  \nJie Liu  \nNational University of Defense Technology Changsha, China [liujie@nudt.edu.cn](liujie@nudt.edu.cn)  \nTiaojie Xiao∗ National University of Defense Technology Changsha, China [xiaotiaojie@nudt.edu.cn](xiaotiaojie@nudt.edu.cn)  \nKeqin Li  \nState University of New York at New Paltz New Paltz, NY, USA [lik@newpaltz.edu](lik@newpaltz.edu)  \nAbstract  \nSimulating two-dimensional frustrated quantum matter is a grand challenge due to the sign problem and exponential Hilbert space complexity. In this work, we introduce the Holographic Quantum Transformer (HQT), a physics-inspired generative architecture that leverages global self-attention to resolve non-local entanglement patterns. We validate HQT on the square lattice 􀀟1 − 􀀟2 Heisenberg model. On the heavily frustrated 8 × 8 lattice at the quantum critical point (􀀟2 = 0. 5), HQT reaches a ground-state energy per site (􀀚/􀀣 ) of −0 .5001 (1), consistent with the expected finite-size scaling trend. Beyond numerical accuracy, HQT exhibits intrinsic physical awareness, autonomously recovering the underlying 􀀟2 interaction geometry through interpretable attention maps. Our central contribution is “Holographic Transfer\", a zero-shot sizeextrapolation protocol with rapid alignment: a model trained on 8×8 systems is directly projected onto larger 10 × 10 lattices via continuous positional-embedding interpolation and head re-initialization, achieving high-fidelity initialization and rapid convergence. This zero-shot protocol yields an energy of 􀀚/􀀣 = −0 .49782 (3), statistically consistent with the variational state ofthe art while requiring no from-scratch training on the target lattice. Our results establish generative attention as a scalable paradigm for transferable quantum simulation.  \nCCS Concepts  \n• Computing methodologies → Neural networks; Machine learning; • Applied computing → Physics.  \nKeywords  \nNeural Quantum States, Transformer, Frustrated Magnetism, Transfer Learning, Quantum Phase Transition, Variational Monte Carlo, AI for Science.  \n∗ Corresponding author.  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. KDD’26, Jeju Island, Republic of Korea  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2259-2/2026/08  \n[https://doi.org/10.1145/3770855.3818932](https://doi.org/10.1145/3770855.3818932)  \nACM Reference Format:  \nXingran Guo, Tiaojie Xiao, Jie Liu, and Keqin Li. 2026. Holographic Quantum Transformer: A Generalist Neuro-Symbolic Architecture for Solving Frustrated Systems via Generative Attention. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD’26), August 09–13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 10 pages. [https://doi.org/10.1145/3770855.3818932](https://doi.org/10.1145/3770855.3818932)  \n1 Introduction  \nAs originally envisioned by Feynman [8], simulating quantum physics with classical computers faces an exponential barrier: the Hilbert space dimension grows as 2􀀣 . While quantum hardware has made strides in error mitigation [1], verifiable advantage on practical many-body problems remains limited. Consequently, classical simulation algorithms continue to serve as the primary engine for discovery.  \nHowever, traditional numerical methods encounter fundamental barriers in two-dimensional (2D) frustrated systems. Tensor Network methods like PEPS and DMRG [30] are constrained by the Area Law of entanglement [7], limiting simulations to narrow cylinders. Conversely, Quantum Monte Carlo (QMC), the workhorse for unfrustrated systems, hits a hard wall in frustrated regimes (e.g., the 􀀟1 − 􀀟2 model) due to the “Sign Problem”. As pro","cbCaie3PMrJbRQnl","https://ap.wps.com/l/cbCaie3PMrJbRQnl","pdf",10947027,1,10,"English","en",105,"# Introduction\n## Quantum simulation challenges in frustrated systems\n## Neural quantum states and deep network architectures\n## Motivation for global Transformer-based attention\n## Overview of the HQT framework","[{\"question\":\"What problem does the Holographic Quantum Transformer (HQT) target?\",\"answer\":\"It targets classical simulation of two-dimensional frustrated quantum matter, where the sign problem and exponential Hilbert space complexity make conventional methods difficult.\"},{\"question\":\"How does HQT handle non-local entanglement in frustrated systems?\",\"answer\":\"HQT uses global self-attention in a holographic embedding/encoder design to resolve the non-local entanglement patterns associated with frustration.\"},{\"question\":\"What is “Holographic Transfer” and how does it work?\",\"answer\":\"It is a zero-shot size-extrapolation protocol that transfers a model trained on an 8×8 lattice to larger 10×10 lattices via positional-embedding interpolation and head re-initialization, avoiding from-scratch 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problem does the Holographic Quantum Transformer (HQT) target?","Question",{"text":75,"@type":76},"It targets classical simulation of two-dimensional frustrated quantum matter, where the sign problem and exponential Hilbert space complexity make conventional methods difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does HQT handle non-local entanglement in frustrated systems?",{"text":80,"@type":76},"HQT uses global self-attention in a holographic embedding/encoder design to resolve the non-local entanglement patterns associated with frustration.",{"name":82,"@type":73,"acceptedAnswer":83},"What is “Holographic Transfer” and how does it work?",{"text":84,"@type":76},"It is a zero-shot size-extrapolation protocol that transfers a model trained on an 8×8 lattice to larger 10×10 lattices via positional-embedding interpolation and head re-initialization, avoiding from-scratch 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