Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
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| Format: | Preprint |
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2026
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| _version_ | 1866908990060888064 |
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| author | Viteritti, Luciano Loris Rende, Riccardo Roth, Christopher Sengupta, Anirvan Carleo, Giuseppe Georges, Antoine |
| author_facet | Viteritti, Luciano Loris Rende, Riccardo Roth, Christopher Sengupta, Anirvan Carleo, Giuseppe Georges, Antoine |
| contents | The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its ground state. We show that a key source of such discrepancies is the bias encoded in the variational ansatz. We consider three different Transformer backflow fermionic wave functions based on a Slater determinant, its particle-hole counterpart, and a Pfaffian, initialized without any mean-field pretraining. We show that, despite achieving nearly degenerate, state-of-the-art variational energies, each ansatz converges to a state with qualitatively different spin, charge, and pairing correlations. Upon improving accuracy via symmetry restoration and variance reduction, however, all three converge to the same physical picture: coexisting superconducting and stripe orders. These results demonstrate that variational energy alone is insufficient to identify the ground state in the presence of competing phases, and highlight the importance of tracking how correlation functions evolve as the wave function is systematically improved before drawing physical conclusions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_21978 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model Viteritti, Luciano Loris Rende, Riccardo Roth, Christopher Sengupta, Anirvan Carleo, Giuseppe Georges, Antoine Strongly Correlated Electrons Disordered Systems and Neural Networks Superconductivity The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its ground state. We show that a key source of such discrepancies is the bias encoded in the variational ansatz. We consider three different Transformer backflow fermionic wave functions based on a Slater determinant, its particle-hole counterpart, and a Pfaffian, initialized without any mean-field pretraining. We show that, despite achieving nearly degenerate, state-of-the-art variational energies, each ansatz converges to a state with qualitatively different spin, charge, and pairing correlations. Upon improving accuracy via symmetry restoration and variance reduction, however, all three converge to the same physical picture: coexisting superconducting and stripe orders. These results demonstrate that variational energy alone is insufficient to identify the ground state in the presence of competing phases, and highlight the importance of tracking how correlation functions evolve as the wave function is systematically improved before drawing physical conclusions. |
| title | Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model |
| topic | Strongly Correlated Electrons Disordered Systems and Neural Networks Superconductivity |
| url | https://arxiv.org/abs/2604.21978 |