Learning quantum disentanglement scheduling from reduced states via modular hybrid policies

• URL: http://arxiv.org/abs/2604.28009v1
• Date: Thu, 30 Apr 2026 15:29:32 GMT
• Title: Learning quantum disentanglement scheduling from reduced states via modular hybrid policies
• Abstract summary: Quantum control with restricted state access is central to near-term quantum devices, where full wave-function information is unavailable.<n>We study this problem through multiqubit disentanglement scheduling from partial observations.<n>We introduce a modular hybrid quantum-classical policy framework consisting of classical preprocessing, a parameterized quantum circuit as a compact nonlinear latent block, and classical postprocessing for pair-selection probabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum control with restricted state access is central to near-term quantum devices, where full wave-function information is unavailable. We study this problem through multiqubit disentanglement scheduling from partial observations, where a controller receives only two-qubit reduced density matrices and selects which qubit pair to disentangle at each step. We introduce a modular hybrid quantum--classical policy framework consisting of classical preprocessing, a parameterized quantum circuit as a compact nonlinear latent block, and classical postprocessing for pair-selection probabilities. Benchmarking 4-, 5-, and 6-qubit tasks, we find that preprocessing is the dominant factor governing performance under reduced-state observations, while the quantum module provides a conditional compact representation whose utility depends on the input features and model budget. We further identify a performance--efficiency trade-off across policy families and find that increasing circuit width is generally more useful than increasing depth. These results provide practical design principles for hybrid policies in reduced-information quantum control.

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