Trade-off between bagging and boosting for quantum separability-entanglement classification

Certifying whether an arbitrary quantum system is entangled or not, is, in general, an NP-hard problem. Though various necessary and sufficient conditions have already been explored in this regard for lower-dimensional systems, it is hard to extend them to higher dimensions. Recently, an ensemble ba...

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Bibliographic Details
Published in:Quantum information processing 2024-07, Vol.23 (7), Article 273
Main Authors: Mohanty, Sanuja D., Patro, Ram N., Biswal, Pradyut K., Pradhan, Biswajit, Sazim, Sk
Format: Article
Language:English
Subjects:
Accuracy
Bagging
Classification
Convexity
Data Structures and Information Theory
Ensemble learning
Machine learning
Mathematical Physics
Physics
Physics and Astronomy
Quantum Computing
Quantum entanglement
Quantum Information Technology
Quantum Physics
Quantum theory
Qubits (quantum computing)
Spintronics
Tradeoffs
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Summary:Certifying whether an arbitrary quantum system is entangled or not, is, in general, an NP-hard problem. Though various necessary and sufficient conditions have already been explored in this regard for lower-dimensional systems, it is hard to extend them to higher dimensions. Recently, an ensemble bagging and convex hull approximation (CHA) approach (together, BCHA) was proposed and it strongly suggests employing a machine learning technique for the separability-entanglement classification problem. However, BCHA does only incorporate the balanced dataset for classification tasks which results in lower average accuracy. In order to solve the data imbalance problem in the present literature, an exploration of the boosting technique has been carried out, and a trade-off between the boosting and bagging-based ensemble classifier is explored for quantum separability problems. For the two-qubit and two-qutrit quantum systems, the pros and cons of the proposed random under-sampling boost CHA (RUSBCHA) for the quantum separability problem are compared with the state-of-the-art CHA and BCHA approaches. As the data are highly unbalanced, performance measures such as overall accuracy, average accuracy, F-measure, and G-mean are evaluated for a fair comparison. The outcomes suggest that RUSBCHA is an alternative to the BCHA approach. Also, for several cases, performance improvements are observed for RUSBCHA since the data are imbalanced.
ISSN:1573-1332
1570-0755
1573-1332
DOI:10.1007/s11128-024-04469-9