Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching

Modern technology and scientific experiments increasingly generate larger and larger amounts of data. This data is sometimes redundant, incomplete or inaccurate and needs to be cleaned and merged with other data before becoming useful for scientific exploration. Hence, entity matching, i.e. the process of linking data about a given entity gathered from multiple data sets, is a major problem in artificial intelligence with applications in science and industry. Typical methods for entity matching either use specialized algorithms or supervised machine learning. Although the problem has been well studied on classical computers, it is unclear how quantum approaches would tackle these challenges. In this paper, we evaluate quantum machine learning algorithms for entity matching on a hand-crafted data set and compare them to similar classical algorithms. We do this by implementing a neural network with a classical embedding layer and extending it with quantum layers. Our experimental results suggest that our hybrid quantum neural network reaches similar performance as classical approaches while requiring an order of magnitude fewer parameters than its classical counterpart. Furthermore, we also show that a model trained on a quantum simulator is portable and thus transferable to a real quantum computer. From a practical perspective and as long as quantum hardware is a scarce resource, experiments, e.g. addressing performance, can profit from producing good initial configurations for quantum neural networks via a simulator, thus only leaving the fine-tuning to quantum computations.