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arXiv · 2107.02301

Machine-Learning-Derived Entanglement Witnesses

Alexander C. B. Greenwood·Larry T. H. Wu·Eric Y. Zhu·Brian T. Kirby·Li Qian
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Abstract

In this work, we show a correspondence between linear support vector machines (SVMs) and entanglement witnesses, and use this correspondence to generate entanglement witnesses for bipartite and tripartite qubit (and qudit) target entangled states. An SVM allows for the construction of a hyperplane that clearly delineates between separable states and the target entangled state; this hyperplane is a weighted sum of observables ('features') whose coefficients are optimized during the training of the SVM. We demonstrate with this method the ability to obtain witnesses that require only local measurements even when the target state is a non-stabilizer state. Furthermore, we show that SVMs are flexible enough to allow us to rank features, and to reduce the number of features systematically while bounding the inference error. This allows us to derive W state witnesses capable of detecting entanglement with fewer measurement terms than the fidelity method dominant in today's literature. The utility of this approach is demonstrated on quantum hardware furnished through the IBM Quantum Experience.

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Alexander C. B. Greenwood, Larry T. H. Wu, Eric Y. Zhu, Brian T. Kirby, Li Qian. 2021-07-05. Machine-Learning-Derived Entanglement Witnesses. https://doi.org/10.1103/physrevapplied.19.034058

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