Browsing by Author "Lehtonen, Leevi"
Now showing items 1-2 of 2
-
Lehtonen, Leevi (2021)Quantum computing has an enormous potential in machine learning, where problems can quickly scale to be intractable for classical computation. A Boltzmann machine is a well-known energy-based graphical model suitable for various machine learning tasks. Plenty of work has already been conducted for realizing Boltzmann machines in quantum computing, all of which have somewhat different characteristics. In this thesis, we conduct a survey of the state-of-the-art in quantum Boltzmann machines and their training approaches. Primarily, we examine variational quantum Boltzmann machine, a specific variant of quantum Boltzmann machine suitable for the near-term quantum hardware. Moreover, as variational quantum Boltzmann machine heavily relies on variational quantum imaginary time evolution, we effectively analyze variational quantum imaginary time evolution to a great extent. Compared to the previous work, we evaluate the execution of variational quantum imaginary time evolution with a more comprehensive collection of hyperparameters. Furthermore, we train variational quantum Boltzmann machines using a toy problem of bars and stripes, representing more multimodal probability distribution than the Bell states and the Greenberger-Horne-Zeilinger states considered in the earlier studies.
-
Lehtonen, Leevi (2021)Sex differences can be found in most human phenotypes, and they play an important role in human health and disease. Females and males have different sex chromosomes, which are known to cause sex differences, as are differences in the concentration of sex hormones such as testosterone, estradiol and progesterone. However, the role of the autosomes has remained more debated. The primary aim of this thesis is to assess the magnitude and relevance of human sex-specific genetic architecture in the autosomes. This is done by calculating sex-specific heritability estimates and genetic correlation estimates between females and males, as well as comparing these to sex differences on the phenotype level. Additionally, the heritability and genetic correlation estimates are compared between two populations, in order to assess the magnitude of sex differences compared to differences between populations. The analyses in this thesis are based on sex-stratified genome-wide association study (GWAS) data from 48 phenotypes in the UK Biobank (UKB), which contains genotype data from approximately 500 000 individuals as well as thousands of phenotype measurements. A replication of the analyses using three phenotypes was also made on data from the FinnGen project, with a dataset from approximately 175 000 individuals. The 48 phenotypes used in this study range from biomarkers such as serum testosterone and albumin levels to general traits such as height and blood pressure. The heritability and genetic correlation estimates were calculated using linkage disequilibrium score regression (LDSC). LDSC fits a linear regression model between test statistic values of GWAS variants and linkage disequilibrium (LD) scores calculated from a reference population. For most phenotypes, the heritability and genetic correlation results show little evidence of sex differences. Serum testosterone level and waist-to-hip ratio are exceptions to this, showing strong evidence of sex differences both on the genetic and the phenotype level. However, the overall correlation between phenotype level sex differences and sex differences in heritability or genetic correlation estimates is low. The replication in the FinnGen dataset for height, weight and body mass index (BMI), showed that for these traits the differences in heritability estimates and genetic correlations between the Finnish and UK populations are comparable or larger than the differences found between males and females.
Now showing items 1-2 of 2