Experimental quantum kernel trick with nuclear spins in a solid

Abstract The kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spaces in those experiments have been smaller than the number of data, which makes them lose their computational advantage over explicit method. Here we show the first experimental demonstration of a quantum kernel machine that achieves a scheme where the dimension of feature space greatly exceeds the number of data using 1H nuclear spins in solid. The use of NMR allows us to obtain the kernel values with single-shot experiment. We employ engineered dynamics correlating 25 spins which is equivalent to using a feature space with a dimension over 1015. This work presents a quantum machine learning using one of the largest quantum systems to date

Experimental quantum kernel trick with nuclear spins in a solid

The kernel trick allows us to employ high-dimensional feature space for a machine learning task without explicitly storing features. Recently, the idea of utilizing quantum systems for computing kernel functions using interference has been demonstrated experimentally. However, the dimension of feature spaces in those experiments have been smaller than the number of data, which makes them lose their computational advantage over explicit method. Here we show the first experimental demonstration of a quantum kernel machine that achieves a scheme where the dimension of feature space greatly exceeds the number of data using 1H nuclear spins in solid. The use of NMR allows us to obtain the kernel values with single-shot experiment. We employ engineered dynamics correlating 25 spins which is equivalent to using a feature space with a dimension over 1015. This work presents a quantum machine learning using one of the largest quantum systems to date

Formation of lipid droplets induced by 2,3-dihydrogeranylgeranoic acid distinct from geranylgeranoic acid

Geranylgeranoic acid (GGA) and 2,3-dihydrogeranylgeranoic acid (2,3-diGGA) are geranylgeraniol-derived metabolites (Kodaira et al. (2002) J Biochem 132: 327-334). In the present study, we examined the effects of these acids on HL-60 cells. The cells were differentiated into neutrophils by GGA stimulation like retinoic acid stimulation. In the case of cells stimulated with 2,3-diGGA, neutrophils were not detected, but the formation of lipid droplets was induced. On the other hand, when the cells were cultured in the presence of 0.1% FBS instead of 10% FBS, apoptotic cells were induced not only by GGA stimulation but also with 2,3-diGGA. In the latter case, when the cells were cultured in the co-presence of a caspase-3 inhibitor (Ac-DMQD-CHO), the lipid droplets formation was observed in the cells. These results suggest that GGA and 2,3-diGGA are extremely different from each other with respect to their effects on HL-60 cells