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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/2511.16772 |
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| _version_ | 1866912721835917312 |
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| author | Montañà-López, Jordi A. Elben, Andreas Choi, Joonhee Trivedi, Rahul |
| author_facet | Montañà-López, Jordi A. Elben, Andreas Choi, Joonhee Trivedi, Rahul |
| contents | As quantum simulators are scaled up to larger system sizes and lower noise rates, non-Markovian noise channels are expected to become dominant. While provably efficient protocols for Markovian models of quantum simulators, either closed system models (described by a Hamiltonian) or open system models (described by a Lindbladian), have been developed, it remains less well understood whether similar protocols for non-Markovian models exist. In this paper, we consider geometrically local lattice models with both quantum and classical non-Markovian noise and show that, under a Gaussian assumption on the noise, we can learn the noise with sample complexity scaling logarithmically with the system size. Our protocol requires preparing the simulator qubits initially in a product state, introducing a layer of single-qubit Clifford gates and measuring product observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficiently learning non-Markovian noise in many-body quantum simulators Montañà-López, Jordi A. Elben, Andreas Choi, Joonhee Trivedi, Rahul Quantum Physics As quantum simulators are scaled up to larger system sizes and lower noise rates, non-Markovian noise channels are expected to become dominant. While provably efficient protocols for Markovian models of quantum simulators, either closed system models (described by a Hamiltonian) or open system models (described by a Lindbladian), have been developed, it remains less well understood whether similar protocols for non-Markovian models exist. In this paper, we consider geometrically local lattice models with both quantum and classical non-Markovian noise and show that, under a Gaussian assumption on the noise, we can learn the noise with sample complexity scaling logarithmically with the system size. Our protocol requires preparing the simulator qubits initially in a product state, introducing a layer of single-qubit Clifford gates and measuring product observables. |
| title | Efficiently learning non-Markovian noise in many-body quantum simulators |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2511.16772 |