Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects

• URL: http://arxiv.org/abs/2509.22758v1
• Date: Fri, 26 Sep 2025 10:40:44 GMT
• Title: Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
• Authors: Ali Abu-Nada, Subhashish Banerjee,
• Abstract summary: We present a emphnovel and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory.<n>A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $langle Z_(S)(t)rangle$ without state tomography or knowledge of the bath.<n>To quantify memory, we introduce a normalized revival-based metric that counts upward 'turn-backs' in emphpredicted $langle Z_(S)(
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)}(t)\rangle$ without state tomography or knowledge of the bath. To quantify memory, we introduce a normalized revival-based metric that counts upward 'turn-backs' in \emph{predicted} $\langle Z_{(S)}(t)\rangle$ and reports the fraction of evaluated samples that exceeds a small threshold. This bounded score provides an interpretable, model-independent indicator of non-Markovianity. We demonstrate the method on two representative noise channels, non-unital amplitude damping and unital dephasing from random telegraph noise (RTN). Under matched conditions, the model accurately reproduces the dynamics and flags memory effects, with RTN exhibiting a larger normalized revival score than amplitude damping. Overall, the approach is experimentally realistic and readily extensible, enabling real-time, interpretable non-Markovian diagnostics from accessible local measurements.

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