Temporal Coarse Graining for Classical Stochastic Noise in Quantum Systems

• URL: http://arxiv.org/abs/2502.12296v1
• Date: Mon, 17 Feb 2025 20:09:18 GMT
• Title: Temporal Coarse Graining for Classical Stochastic Noise in Quantum Systems
• Authors: Tameem Albash, Steve Young, N. Tobias Jacobson,
• Abstract summary: We present an approach for simulating Hamiltonian classical noise that performs temporal coarse-graining. We focus on the case where the noise can be expressed as a sum of Ornstein-Uhlenbeck processes. For Ornstein-Uhlenbeck processes, the deterministic components capture all dependence on the coarse realization.
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• Abstract: Simulations of quantum systems with Hamiltonian classical stochastic noise can be challenging when the noise exhibits temporal correlations over a multitude of time scales, such as for $1/f$ noise in solid-state quantum information processors. Here we present an approach for simulating Hamiltonian classical stochastic noise that performs temporal coarse-graining by effectively integrating out the high-frequency components of the noise. We focus on the case where the stochastic noise can be expressed as a sum of Ornstein-Uhlenbeck processes. Temporal coarse-graining is then achieved by conditioning the stochastic process on a coarse realization of the noise, expressing the conditioned stochastic process in terms of a sum of smooth, deterministic functions and bridge processes with boundaries fixed at zero, and performing the ensemble average over the bridge processes. For Ornstein-Uhlenbeck processes, the deterministic components capture all dependence on the coarse realization, and the stochastic bridge processes are not only independent but taken from the same distribution with correlators that can be expressed analytically, allowing the associated noise propagators to be precomputed once for all simulations. This combination of noise trajectories on a coarse time grid and ensemble averaging over bridge processes has practical advantages, such as a simple concatenation rule, that we highlight with numerical examples.

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