Neural Quantum States in Non-Stabilizer Regimes: Benchmarks with Atomic Nuclei
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917369872384000 |
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| author | Keeble, James W. T. Lovato, Alessandro Robin, Caroline E. P. |
| author_facet | Keeble, James W. T. Lovato, Alessandro Robin, Caroline E. P. |
| contents | As neural networks are known to efficiently represent classes of tensor-network states as well as volume-law-entangled states, identifying which properties determine the representational capabilities of neural quantum states (NQS) remains an open question. We construct NQS representations of ground states of medium-mass atomic nuclei, which typically exhibit significant entanglement and non-stabilizerness, to study their performance in relation to the quantum complexity of the target state. Leveraging a second-quantized formulation of NQS tailored for nuclear-physics applications, we perform calculations in active orbital spaces using a restricted Boltzmann machine (RBM), a prototypical NQS ansatz. For a fixed number of configurations, we find that states with larger non-stabilizerness are systematically harder to learn, as evidenced by reduced accuracy. This finding suggests that non-stabilizerness is a primary factor governing the compression and representational efficiency of RBMs in entangled regimes, and motivates extending these studies to more sophisticated network architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28646 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neural Quantum States in Non-Stabilizer Regimes: Benchmarks with Atomic Nuclei Keeble, James W. T. Lovato, Alessandro Robin, Caroline E. P. Nuclear Theory Quantum Physics As neural networks are known to efficiently represent classes of tensor-network states as well as volume-law-entangled states, identifying which properties determine the representational capabilities of neural quantum states (NQS) remains an open question. We construct NQS representations of ground states of medium-mass atomic nuclei, which typically exhibit significant entanglement and non-stabilizerness, to study their performance in relation to the quantum complexity of the target state. Leveraging a second-quantized formulation of NQS tailored for nuclear-physics applications, we perform calculations in active orbital spaces using a restricted Boltzmann machine (RBM), a prototypical NQS ansatz. For a fixed number of configurations, we find that states with larger non-stabilizerness are systematically harder to learn, as evidenced by reduced accuracy. This finding suggests that non-stabilizerness is a primary factor governing the compression and representational efficiency of RBMs in entangled regimes, and motivates extending these studies to more sophisticated network architectures. |
| title | Neural Quantum States in Non-Stabilizer Regimes: Benchmarks with Atomic Nuclei |
| topic | Nuclear Theory Quantum Physics |
| url | https://arxiv.org/abs/2603.28646 |