Quantum artificial vision for defect detection in manufacturing

In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.Comment: 11 pages, 7 figures, 16 tables, revised versio

Do Italian consumers value health claims on extra-virgin olive oil?

The present study aims to extend the existing literature on EVOO consumers by assessing the importance of health claims in selecting EVOO products by jointly accounting for consumer preferences for the many search, experience, and credence attributes potentially available on EVOO, as well as by accounting for attitudinal and psychographic individual characteristics which affect consumer decision to prefer products with health claims over conventional ones (i.e. subjective nutritional knowledge, nutritional knowledge, nutritional importance, attitudes towards using food as a medicine, general health interest). The latter characteristics play a pivotal role in individual decisions to consider health claims when purchasing food, as indicated in the general literature on consumers and health claims. Such individual related characteristics capture the individual attitude and interest in preventing health losses through food choices and diet (see for instance, Roininen et al. (1999), Van Trijp & Van der Lans (2007) Dean et al. (2012)). To achieve our research goal, we employed a best-worst (BW) approach on a representative sample of Italian household members who are responsible for food shopping. Consumers tested competitively the multiple product attributes of EVOO. The share of consumers interested in health claims was detected by a latent class clustering model and characterized based on their socio-demographic, attitudinal, and psychographic features. Thus, this is the first study attempting to identify the features of consumers interested in health claims while purchasing EVOO using a large sample of household responsible of food purchases

Financial Risk Management on a Neutral Atom Quantum Processor

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.Comment: 17 pages, 11 figures, 2 tables, revised versio