Functional Neural Wavefunction Optimization
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915389986832384 |
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| author | Armegioiu, Victor Carrasquilla, Juan Mishra, Siddhartha Müller, Johannes Nys, Jannes Zeinhofer, Marius Zhang, Hang |
| author_facet | Armegioiu, Victor Carrasquilla, Juan Mishra, Siddhartha Müller, Johannes Nys, Jannes Zeinhofer, Marius Zhang, Hang |
| contents | We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function space. The framework translates infinite-dimensional optimization dynamics into tractable parameter-space algorithms through a Galerkin projection onto the tangent space of the variational ansatz. This perspective unifies existing methods such as stochastic reconfiguration and Rayleigh-Gauss-Newton, provides connections to classic function-space algorithms, and motivates the derivation of novel algorithms with geometrically principled hyperparameter choices. We validate our framework with numerical experiments demonstrating its practical relevance through the accurate estimation of ground-state energies for several prototypical models in condensed matter physics modeled with neural network wavefunctions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Functional Neural Wavefunction Optimization Armegioiu, Victor Carrasquilla, Juan Mishra, Siddhartha Müller, Johannes Nys, Jannes Zeinhofer, Marius Zhang, Hang Strongly Correlated Electrons Machine Learning Optimization and Control Computational Physics Quantum Physics We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function space. The framework translates infinite-dimensional optimization dynamics into tractable parameter-space algorithms through a Galerkin projection onto the tangent space of the variational ansatz. This perspective unifies existing methods such as stochastic reconfiguration and Rayleigh-Gauss-Newton, provides connections to classic function-space algorithms, and motivates the derivation of novel algorithms with geometrically principled hyperparameter choices. We validate our framework with numerical experiments demonstrating its practical relevance through the accurate estimation of ground-state energies for several prototypical models in condensed matter physics modeled with neural network wavefunctions. |
| title | Functional Neural Wavefunction Optimization |
| topic | Strongly Correlated Electrons Machine Learning Optimization and Control Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2507.10835 |