Reverse engineering pairwise entanglement.

Designing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process is "learned," thus obviating the algorithm design difficulty. This works quite well. But it is also of interest to explore what exactly these learned "algorithms" are. Here, we take a step towards answering that question, by looking at a learned entanglement witness for a two-qubit system, and find its decomposition in terms of elementary quantum gates such as can be implemented on one of the commercially available gate model quantum computer simulators. The learned "algorithm" generalizes easily to three-, four-, five-, six-, and seven-qubit systems, and we infer a result for mesoscopic N. We successfully implement our witness on Microsoft's Q\#. Our results suggest a fruitful pathway for general quantum computer algorithm design.