Efficiently learning non-Markovian noise in many-body quantum simulators

• URL: http://arxiv.org/abs/2511.16772v1
• Date: Thu, 20 Nov 2025 19:56:27 GMT
• Title: Efficiently learning non-Markovian noise in many-body quantum simulators
• Authors: Jordi A. Montañà-López, Andreas Elben, Joonhee Choi, Rahul Trivedi,
• Abstract summary: We consider geometrically local lattice models with both quantum and classical non-Markovian noise.<n>We show that, under a Gaussian assumption on the noise, we can learn the noise with sample complexity scaling logarithmically with the system size.<n>Our protocol requires preparing the simulator qubits initially in a product state, introducing a layer of single-qubit Clifford gates and measuring product observables.
• Score: 2.52748973579828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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