AI Article Synopsis
- This study addresses two significant issues in the stochastic modeling of open quantum systems.
- It identifies classical stochastic processes linked to non-Markovian quantum state diffusion and quantum jumps in Hilbert space.
- Additionally, it introduces a new type of diffusive process, called non-Markovian quantum diffusion, while also enhancing the practicality of existing models through a kernel smoothing technique.
Article Abstract
We solve two long-standing problems for stochastic descriptions of open quantum system dynamics. First, we find the classical stochastic processes corresponding to non-Markovian quantum state diffusion and non-Markovian quantum jumps in projective Hilbert space. Second, we show that the diffusive limit of non-Markovian quantum jumps can be taken on the projective Hilbert space in such a way that it coincides with non-Markovian quantum state diffusion. However, the very same limit taken on the Hilbert space leads to a completely new diffusive unraveling, which we call non-Markovian quantum diffusion. Further, we expand the applicability of non-Markovian quantum jumps and non-Markovian quantum diffusion by using a kernel smoothing technique allowing a significant simplification in their use. Lastly, we demonstrate the applicability of our results by studying a driven dissipative two level atom in a non-Markovian regime using all of the three methods.
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| http://dx.doi.org/10.1103/PhysRevLett.125.150403 | DOI Listing |
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Article Synopsis
- This study introduces a machine learning-based solver for accurately simulating dissipative quantum dynamics in open quantum systems.
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