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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.13877 |
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| _version_ | 1866910131562741760 |
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| author | Xiao, Yu-Cheng Chang, Jen-Yu Kuo, Tzu-Ling Astuti, Aninda Wu, Shu-Chi Ng, Ka-Lok Wang, Yun-Yuan Chen, Yu-Ze Chen, Nan-Yow Li, Tai-Yu |
| author_facet | Xiao, Yu-Cheng Chang, Jen-Yu Kuo, Tzu-Ling Astuti, Aninda Wu, Shu-Chi Ng, Ka-Lok Wang, Yun-Yuan Chen, Yu-Ze Chen, Nan-Yow Li, Tai-Yu |
| contents | We propose Scalable Quantum Molecular Generation (SQMG), a variational quantum-circuit for sampling molecular graphs using chemical priors on atoms and bonds. SQMG assigns a fixed 3-qubit register to each heavy atom and reuses a single 2-qubit bond register to generate bonds sequentially, yielding an ''atom no-reuse, bond reuse'' architecture with linear qubit scaling. Measurement results are mapped to molecular graphs via lightweight classical decoding with structural constraints. In CUDA-Q, we benchmark the state-vector simulation (CPU/GPU) and the tensor-network simulation (GPU). At $N=8$ heavy atoms, the state-vector simulator (GPU) and the tensor-network simulator (GPU) achieve speeds of up to $4.5\times 10^{4}$ and $2.2\times 10^{3}$ over the state-vector (CPU) baseline, respectively. Crucially, tensor-network simulation extends exact simulation to $N=40$ heavy atoms, where state-vector methods become memory-limited. For training, Bayesian optimization outperforms COBYLA on a Validity$\times$Uniqueness objective, and the same architecture supports \textit{de novo} generation, scaffold decoration, and linker design. Overall, SQMG provides a scalable, reproducible testbed for evaluating accelerated tensor-network simulation and future quantum molecular generation algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13877 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Quantum Molecular Generation via GPU-Accelerated Tensor-Network Simulation Xiao, Yu-Cheng Chang, Jen-Yu Kuo, Tzu-Ling Astuti, Aninda Wu, Shu-Chi Ng, Ka-Lok Wang, Yun-Yuan Chen, Yu-Ze Chen, Nan-Yow Li, Tai-Yu Quantum Physics We propose Scalable Quantum Molecular Generation (SQMG), a variational quantum-circuit for sampling molecular graphs using chemical priors on atoms and bonds. SQMG assigns a fixed 3-qubit register to each heavy atom and reuses a single 2-qubit bond register to generate bonds sequentially, yielding an ''atom no-reuse, bond reuse'' architecture with linear qubit scaling. Measurement results are mapped to molecular graphs via lightweight classical decoding with structural constraints. In CUDA-Q, we benchmark the state-vector simulation (CPU/GPU) and the tensor-network simulation (GPU). At $N=8$ heavy atoms, the state-vector simulator (GPU) and the tensor-network simulator (GPU) achieve speeds of up to $4.5\times 10^{4}$ and $2.2\times 10^{3}$ over the state-vector (CPU) baseline, respectively. Crucially, tensor-network simulation extends exact simulation to $N=40$ heavy atoms, where state-vector methods become memory-limited. For training, Bayesian optimization outperforms COBYLA on a Validity$\times$Uniqueness objective, and the same architecture supports \textit{de novo} generation, scaffold decoration, and linker design. Overall, SQMG provides a scalable, reproducible testbed for evaluating accelerated tensor-network simulation and future quantum molecular generation algorithms. |
| title | Scalable Quantum Molecular Generation via GPU-Accelerated Tensor-Network Simulation |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2604.13877 |