Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets

Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.

Effect of resistant starch on the intestinal health of old dogs: fermentation products and histological features of the intestinal mucosa

The effects of resistant starch (RS) intake on nutrient digestibility, microbial fermentation products, faecal IgA, faecal pH, and histological features of the intestinal mucosa of old dogs were evaluated. The same formulation was extruded in two different conditions: one to obtain elevated starch cooking degree with low RS content (0.21%) and the other lower starch cooking with high RS content (1.46%). Eight geriatric Beagles (11.5 ± 0.38 years old) were fed each diet for 61 days in a crossover design. Food intake, nutrient digestibility, fermentation products, faecal pH, and faecal IgA were examined via variance analysis. Histological results of intestinal biopsies were assessed via Wilcoxon test for paired data. The morphometric characteristics of large intestine crypts were evaluated via paired t tests (p < .05). Protein, fat, and energy digestibilities were higher for the low-RS diet (p < .05). Dogs receiving the high-RS diet had lower faecal pH and higher values for propionate, butyrate, total volatile fatty acids, and lactate (p < .05). No differences between diets were found in the histological parameters of the gut mucosa, and only a tendency for deeper crypts in the descending colon was observed for dogs fed the high-RS diet (p = .083). The intake of a corn-based kibble diet manufactured with coarse ground raw material and low starch gelatinization to obtain 1.4% of RS affected microbial fermentation products and faecal pH and tended to increase crypt depth in the descending colon of old dogs.