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arXiv · 2511.16772

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

Jordi A. Montañà-López·Andreas Elben·Joonhee Choi·Rahul Trivedi
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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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Jordi A. Montañà-López, Andreas Elben, Joonhee Choi, Rahul Trivedi. 2025-11-20. Efficiently learning non-Markovian noise in many-body quantum simulators. https://arxiv.org/abs/2511.16772

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