Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866908822829793280 |
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| author | Paine, Annie E. Chaudhary, Smit Gentile, Antonio A. |
| author_facet | Paine, Annie E. Chaudhary, Smit Gentile, Antonio A. |
| contents | Physics-informed (PI) methodologies have surged to become a pillar route to solve Differential Equations (DEs), sustained by the growth of machine learning methods in scientific contexts. The main proposition of PI is to minimise variationally a loss function, formally ensuring that a neural surrogate of the solution has the DE locally satisfied. The nature of such formulation encouraged the exploration of equivalent quantum algorithms, where the surrogate solution is expressed by variational quantum architectures. The locality of typical loss functions emphasises the DE to hold at an ensemble of points sampled in the domain, but encounters issues when generalising beyond such points, or when propagating boundary conditions. Issues which affect classical and quantum PI algorithms alike. The quest to fill this gap in robustness and accuracy against mainstream DE solvers has led to a plethora of proposals in various directions. In particular, classical DE solvers have long employed the weak form - an integral based approach aiming at imposing a global condition on the solution - prioritising a good average behaviour instead of ``overfitting'' select points. Here, we propose and explore to combine contributions from both local and global loss functions in PI routines, to exploit the advantages and mitigate the weaknesses of both. We showcase this intuition in a variety of problems focusing on differentiable quantum architectures, and demonstrating in particular how orchestrating such hybrid loss formulation with domain decomposition can offer a strong advantage over local-only strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08703 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust Paine, Annie E. Chaudhary, Smit Gentile, Antonio A. Quantum Physics Physics-informed (PI) methodologies have surged to become a pillar route to solve Differential Equations (DEs), sustained by the growth of machine learning methods in scientific contexts. The main proposition of PI is to minimise variationally a loss function, formally ensuring that a neural surrogate of the solution has the DE locally satisfied. The nature of such formulation encouraged the exploration of equivalent quantum algorithms, where the surrogate solution is expressed by variational quantum architectures. The locality of typical loss functions emphasises the DE to hold at an ensemble of points sampled in the domain, but encounters issues when generalising beyond such points, or when propagating boundary conditions. Issues which affect classical and quantum PI algorithms alike. The quest to fill this gap in robustness and accuracy against mainstream DE solvers has led to a plethora of proposals in various directions. In particular, classical DE solvers have long employed the weak form - an integral based approach aiming at imposing a global condition on the solution - prioritising a good average behaviour instead of ``overfitting'' select points. Here, we propose and explore to combine contributions from both local and global loss functions in PI routines, to exploit the advantages and mitigate the weaknesses of both. We showcase this intuition in a variety of problems focusing on differentiable quantum architectures, and demonstrating in particular how orchestrating such hybrid loss formulation with domain decomposition can offer a strong advantage over local-only strategies. |
| title | Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.08703 |