ABSTRACT
We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main contribution, index slicing, is a method that efficiently parallelizes the contraction by breaking it down into much smaller and identically structured subtasks, which can then be executed in parallel without dependencies. We benchmark our algorithm on a class of random quantum circuits, achieving greater than 105 times acceleration over the original estimate of the simulation cost. We then demonstrate applications of the simulation framework for aiding the development of quantum algorithms and quantum error correction. As tensor networks are widely used in computational science, our simulation framework may find further applications.
ABSTRACT
Cocoon and silk yields are the most important characteristics of sericulture. However, few studies have examined the genes that modulate these features. Further studies of these genes will be useful for improving the products of sericulture. JingSong (JS) and Lan10 (L10) are two strains having significantly different cocoon and silk yields. In the current study, RNA-Seq and quantitative polymerase chain reaction (qPCR) were performed on both strains in order to determine divergence of the silk gland, which controls silk biosynthesis in silkworms. Compared with L10, JS had 1375 differentially expressed genes (DEGs; 738 up-regulated genes and 673 down-regulated genes). Nine enriched gene ontology (GO) terms were identified by GO enrichment analysis based on these DEGs. KEGG enrichment analysis results showed that the DEGs were enriched in three pathways, which were mainly associated with the processing and biosynthesis of proteins. The representative genes in the enrichment pathways and ten significant DEGs were further verified by qPCR, the results of which were consistent with the RNA-Seq data. Our study has revealed differences in silk glands between the two silkworm strains and provides a perspective for understanding the molecular mechanisms determining silk yield.
