Learning quantum disentanglement scheduling from reduced states via modular hybrid policies

TL;DR

This paper introduces a modular hybrid quantum-classical policy framework for quantum disentanglement scheduling using partial state observations, demonstrating its effectiveness on multi-qubit tasks.

Contribution

It presents a novel hybrid policy architecture combining classical preprocessing, a quantum circuit, and classical postprocessing for quantum control with limited information.

Findings

Preprocessing dominates performance under reduced-state observations.

Quantum module offers a compact representation whose utility varies with input features.

Increasing circuit width generally improves performance more than increasing depth.

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.