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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13971 |
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| _version_ | 1866915853122928640 |
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| author | Macarone-Palmieri, Adriano Ferrara, Alberto Franco, Rosario Lo |
| author_facet | Macarone-Palmieri, Adriano Ferrara, Alberto Franco, Rosario Lo |
| contents | Multipartite entanglement is a crucial resource for quantum technologies; however, its scalable generation in noisy quantum devices remains a significant challenge. Here, we propose a low-depth quantum neural network architecture with linear scaling, employing a novel approach to introducing activation functions for entanglement engineering. As a testbed to demonstrate the clear advantage unlocked by the introduction of nonlinear activations, we run a Monte Carlo sampling over $10^5$ circuit topologies for pure noiseless states. Subsequently, we focus on the noisy scenario; we employ the experimentally accessible Meyer-Wallach global entanglement as a scalable surrogate optimization cost and certify entanglement via bipartite negativity. For 10-qubit mixed states, the optimized circuits generate substantial entanglement across the bipartitions. Lastly, the presence of genuine multipartite entanglement is certified with semi-definite programming. These result establish an experimentally motivated and scalable framework for engineering multipartite entanglement on near-term quantum devices, highlighting the combined role of nonlinearity and circuit topology scaling up to 20 qubits readily. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_13971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A nonlinear quantum neural network framework for entanglement engineering Macarone-Palmieri, Adriano Ferrara, Alberto Franco, Rosario Lo Quantum Physics Multipartite entanglement is a crucial resource for quantum technologies; however, its scalable generation in noisy quantum devices remains a significant challenge. Here, we propose a low-depth quantum neural network architecture with linear scaling, employing a novel approach to introducing activation functions for entanglement engineering. As a testbed to demonstrate the clear advantage unlocked by the introduction of nonlinear activations, we run a Monte Carlo sampling over $10^5$ circuit topologies for pure noiseless states. Subsequently, we focus on the noisy scenario; we employ the experimentally accessible Meyer-Wallach global entanglement as a scalable surrogate optimization cost and certify entanglement via bipartite negativity. For 10-qubit mixed states, the optimized circuits generate substantial entanglement across the bipartitions. Lastly, the presence of genuine multipartite entanglement is certified with semi-definite programming. These result establish an experimentally motivated and scalable framework for engineering multipartite entanglement on near-term quantum devices, highlighting the combined role of nonlinearity and circuit topology scaling up to 20 qubits readily. |
| title | A nonlinear quantum neural network framework for entanglement engineering |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2512.13971 |