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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.17252 |
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| _version_ | 1866909570492792832 |
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| author | Li, Min Lin, Mao Beach, Matthew J. S. |
| author_facet | Li, Min Lin, Mao Beach, Matthew J. S. |
| contents | The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing. We introduce a Resource-Optimized Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer unique quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17252 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resource-Optimized Grouping Shadow for Efficient Energy Estimation Li, Min Lin, Mao Beach, Matthew J. S. Quantum Physics The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing. We introduce a Resource-Optimized Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer unique quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers. |
| title | Resource-Optimized Grouping Shadow for Efficient Energy Estimation |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2406.17252 |