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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.04011 |
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| _version_ | 1866918321983586304 |
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| author | Miller, Benjamin N. Elgee, Peter K. Pruitt, Jason R. Cox, Kevin C. |
| author_facet | Miller, Benjamin N. Elgee, Peter K. Pruitt, Jason R. Cox, Kevin C. |
| contents | The study of quantum circuit simulation using classical computers is a key research topic that helps define the boundary of verifiable quantum advantage, solve quantum many-body problems, and inform development of quantum hardware and software. Tensor networks have become forefront mathematical tools for these tasks. Here we introduce a method to approximately simulate quantum circuits using sparsely-populated tensors. We describe a sparse tensor data structure that can represent quantum states with no underlying symmetry, and outline algorithms to efficiently contract and truncate these tensors. We show that the data structure and contraction algorithm are efficient, leading to expected runtime scalings versus qubit number and circuit depth. Our results motivate future research in optimization of sparse tensor networks for quantum simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04011 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Approximate simulation of complex quantum circuits using sparse tensors Miller, Benjamin N. Elgee, Peter K. Pruitt, Jason R. Cox, Kevin C. Quantum Physics The study of quantum circuit simulation using classical computers is a key research topic that helps define the boundary of verifiable quantum advantage, solve quantum many-body problems, and inform development of quantum hardware and software. Tensor networks have become forefront mathematical tools for these tasks. Here we introduce a method to approximately simulate quantum circuits using sparsely-populated tensors. We describe a sparse tensor data structure that can represent quantum states with no underlying symmetry, and outline algorithms to efficiently contract and truncate these tensors. We show that the data structure and contraction algorithm are efficient, leading to expected runtime scalings versus qubit number and circuit depth. Our results motivate future research in optimization of sparse tensor networks for quantum simulation. |
| title | Approximate simulation of complex quantum circuits using sparse tensors |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2602.04011 |