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“…The data driven approach for predictive dynamics used here and, for instance, in references [19,20] has some clear similarities to both the projection and the collision methods. One defines r t i as the Bloch vector for the i-th qubit at the time t, which is then discretised into t n = n∆ with n an integer and some fundamental time ∆, and sets…”

confidence: 98%

“…The data driven approach for predictive dynamics used here and, for instance, in references [19,20] has some clear similarities to both the projection and the collision methods. One defines r t i as the Bloch vector for the i-th qubit at the time t, which is then discretised into t n = n∆ with n an integer and some fundamental time ∆, and sets…”

confidence: 98%

“…For instance, in reference [19] this data-driven method was applied to learn time-local generators of the dynamics of open quantum systems. Similarly, in [20] a related approach was used to find an effective Markovian embedding from which several important properties and the exact dynamics of the reduced system could be extracted.…”

confidence: 99%

“…The mentioned adjustments and enhancements with multiple small QRs are essential for realistic experimental implementations of QRC based on modern quantum technology 23 . Additionally, QRC may be interpreted as the 'inverse' to recently proposed methods that apply machine learning techniques to learn non-Markovian quantum dynamics of a qubit system in a complex environment 24,25 .…”

confidence: 99%

“…Here, we propose a different approach to a class of problems where a sufficiently small subsystem S of a many-body system is subject to time-dependent controls. Focusing on time evolution of degrees of freedom in S, we represent the rest of the many-body system E by its lower-dimensional "twin", or a reduced-order [10][11][12][13] model. Such a reduction effectively keeps track of the relevant degrees of freedom in E, discarding the ones which have little or no influence on dynamics of S. The reduced-order model may involve effective Hilbert space dimension that is orders of magnitude smaller than the one in the original problem.…”

confidence: 99%