Energetics of a Single Qubit Gate.

Qubits are physical, a quantum gate thus not only acts on the information carried by the qubit but also on its energy. What is then the corresponding flow of energy between the qubit and the controller that implements the gate? Here we exploit a superconducting platform to answer this question in the case of a quantum gate realized by a resonant drive field. During the gate, the superconducting qubit becomes entangled with the microwave drive pulse so that there is a quantum superposition between energy flows. We measure the energy change in the drive field conditioned on the outcome of a projective qubit measurement. We demonstrate that the drive's energy change associated with the measurement backaction can exceed by far the energy that can be extracted by the qubit. This can be understood by considering the qubit as a weak measurement apparatus of the driving field.

Direct and maternal genetic effects on growth, reproduction, and ultrasound traits in zebu Brahman cattle in Colombia.

Covariance components and genetic parameters were estimated for birth weight (BiW); adjusted weights at 4, 7, 12, and 18 mo; and ADG between 0 and 4 mo, between 4 and 7 mo, between 7 and 12 mo, and between 12 and 18 mo. Additionally, reproductive traits, calving interval, and age at first calving were analyzed, together with traits measured by ultrasound: loin eye area, deep fat mean, back fat, and rump fat. Analyses were performed using an animal model, considering the fixed effects of the farm ( = 37), year and month of birth, sex, calving number (1 to 7), season (dry and rainy seasons), region (North Coast, Andean Region, and Oriental Savannas), and conception (natural mating or AI), whereas the age of the cows at calving was considered a polynomial covariate with linear and quadratic effects. Three different models were used to find the one with the best fit for each trait: a single-trait model with an additive direct genetic effect, a single-trait model with additive direct and maternal genetic effects, and finally, a multitrait model with an additive direct genetic effect. For the growth traits, the heritability was between 0.24 and 0.47, with the lowest value for weight at 7 mo and the greatest value for BiW, and the maternal heritability was found to be between 0.15 and 0.21 but did not decrease later on. The correlation between direct and maternal effects was high and negative (-0.59 to -0.76). With ultrasound traits, a model with only direct effects was used. The heritability was between 0.13 and 0.28 for back fat and loin eye area, respectively. The heritabilities for deep fat mean and rump fat were similar, being 0.19 and 0.21, respectively. The reproductive traits showed high residual variance. In particular, the heritability of calving interval was low (0.06). The results showed that the growth traits have an important genetic component, which is a favorable indicator for obtaining improvement progress in the zebu Brahman breed for beef production in tropical regions of Colombia. The maternal effects influenced the weight performance from 4 to 18 mo and should be taken into account in genetic analyses. In general, the direct heritability of medium magnitude estimated for growth and traits measured by ultrasound suggests that individual selection for these traits will be effective.

Multiple trait genetic evaluation of clinical mastitis in three dairy cattle breeds.

In 2010, a routine genetic evaluation on occurrence of clinical mastitis in three main dairy cattle breeds-- Montbéliarde (MO), Normande (NO) and Holstein (HO)--was implemented in France. Records were clinical mastitis events reported by farmers to milk recording technicians and the analyzed trait was the binary variable describing the occurrence of a mastitis case within the first 150 days of the first three lactations. Genetic parameters of clinical mastitis were estimated for the three breeds. Low heritability estimates were found: between 2% and 4% depending on the breed. Despite its low heritability, the trait exhibits genetic variation so efficient genetic improvement is possible. Genetic correlations with other traits were estimated, showing large correlations (often>0.50, in absolute value) between clinical mastitis and somatic cell score (SCS), longevity and some udder traits. Correlation with milk yield was moderate and unfavorable (ρ=0.26 to 0.30). High milking speed was genetically associated with less mastitis in MO (ρ=-0.14) but with more mastitis in HO (ρ=0.18). A two-step approach was implemented for routine evaluation: first, a univariate evaluation based on a linear animal model with permanent environment effect led to pre-adjusted records (defined as records corrected for all non-genetic effects) and associated weights. These data were then combined with similar pre-adjusted records for others traits in a multiple trait BLUP animal model. The combined breeding values for clinical mastitis obtained are the official (published) ones. Mastitis estimated breeding values (EBV) were then combined with SCSs EBV into an udder health index, which receives a weight of 14.5% to 18.5% in the French total merit index (ISU) of the three breeds. Interbull genetic correlations for mastitis occurrence were very high (ρ=0.94) with Nordic countries, where much stricter recording systems exist reflecting a satisfactory quality of phenotypes as reported by the farmers. They were lower (around 0.80) with countries supplying SCS as a proxy for the international evaluation on clinical mastitis.

Short communication: Imputation performances of 3 low-density marker panels in beef and dairy cattle.

Low-density chips are appealing alternative tools contributing to the reduction of genotyping costs. Imputation enables researchers to predict missing genotypes to recreate the denser coverage of the standard 50K (∼50,000) genotype. Two alternative in silico chips were defined in this study that included markers selected to optimize minor allele frequency and spacing. The objective of this study was to compare the imputation accuracy of these custom low-density chips with a commercially available 3K chip. Data consisted of genotypes of 4,037 Holstein bulls, 1,219 Montbéliarde bulls, and 991 Blonde d'Aquitaine bulls. Criteria to select markers to include in low-density marker panels are described. To mimic a low-density genotype, all markers except the markers present on the low-density panel were masked in the validation population. Imputation was performed using the Beagle software. Combining the directed acyclic graph obtained with Beagle with the PHASEBOOK algorithm provides fast and accurate imputation that is suitable for routine genomic evaluations based on imputed genotypes. Overall, 95 to 99% of alleles were correctly imputed depending on the breed and the low-density chip used. The alternative low-density chips gave better results than the commercially available 3K chip. A low-density chip with 6,000 markers is a valuable genotyping tool suitable for both dairy and beef breeds. Such a tool could be used for preselection of young animals or large-scale screening of the female population.

Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations.

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data consisted of genotypes of 15,966 European Holstein bulls from the combined EuroGenomics reference population. Genotypes with the low-density chip were created by erasing markers from 50,000-marker data. The studies were performed in the Nordic countries (Denmark, Finland, and Sweden) using a BLUP model for prediction of DGV and in France using a genomic marker-assisted selection approach for prediction of GEBV. Imputation in both studies was done using a combination of the DAGPHASE 1.1 and Beagle 2.1.3 software. Traits considered were protein yield, fertility, somatic cell count, and udder depth. Imputation of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test data. Mean imputation error rates when using national reference animals was 5.5 and 3.9% in the Nordic countries and France, respectively, whereas imputation based on the EuroGenomics reference data set gave mean error rates of 4.0 and 2.1%, respectively. Prediction of GEBV based on genotypes imputed with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected in the French study, and a loss of 0.06 was observed for the mean reliability of DGV in the Nordic study. Consequently, the reliability of DGV using the imputed SNP data was 0.38 based on national reference data, and 0.48 based on EuroGenomics reference data in the Nordic validation, and the reliability of GEBV using the imputed SNP data was 0.41 based on national reference data, and 0.44 based on EuroGenomics reference data in the French validation.