Detecting Entanglement Graph Structure from Partial Quantum Observations Using Classical Machine Learning

This work investigates whether classical machine learning can detect statistical signatures associated with entanglement graph structure from partial quantum measurements. Experiments were performed on real IBM Quantum hardware using circuits with controlled entanglement patterns. Only a subset of qubits was observed, while the state of one qubit was hidden. A classical Random Forest classifier was trained to predict the hidden qubit’s measurement outcome based solely on the observed subset. To distinguish structural effects from spatial or hardware-related confounders, a geometry decoupling protocol was introduced. This involved comparing entangled circuits with control circuits of identical spatial layout but without entangling gates. Results show that classification accuracy exceeds chance in entangled configurations and correlates negatively with graph distance, suggesting that classical models can capture statistical signatures consistent with entanglement connectivity. The introduced delta metric partially controls for hardware bias by comparing entangled and non-entangled baselines. The observed signal reflects classical correlations in measurement outcomes generated by entangled states and does not constitute quantum communication or violation of no-signalling principles. This dataset and code provide a reproducible experimental framework for studying the relationship between quantum circuit structure and classical detectability using machine learning.