QKAN: Quantum Kolmogorov-Arnold Networks

Autor: Ivashkov, Petr, Huang, Po-Wei, Koor, Kelvin, Pira, Lirandë, Rebentrost, Patrick

The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architectur

Externí odkaz: http://arxiv.org/abs/2410.04435

Data-driven background model for the CUORE experiment

Autor: CUORE Collaboration, Adams, D. Q., Alduino, C., Alfonso, K., Avignone III, F. T., Azzolini, O., Bari, G., Bellini, F., Benato, G., Beretta, M., Biassoni, M., Branca, A., Brofferio, C., Bucci, C., Camilleri, J., Caminata, A., Campani, A., Cao, J., Capelli, S., Capelli, C., Cappelli, L., Cardani, L., Carniti, P., Casali, N., Celi, E., Chiesa, D., Clemenza, M., Cremonesi, O., Creswick, R. J., D'Addabbo, A., Dafinei, I., Del Corso, F., Dell'Oro, S., Di Domizio, S., Di Lorenzo, S., Dixon, T., Dompè, V., Fang, D. Q., Fantini, G., Faverzani, M., Ferri, E., Ferroni, F., Fiorini, E., Franceschi, M. A., Freedman, S. J., Fu, S. H., Fujikawa, B. K., Ghislandi, S., Giachero, A., Girola, M., Gironi, L., Giuliani, A., Gorla, P., Gotti, C., Guillaumon, P. V., Gutierrez, T. D., Han, K., Hansen, E. V., Heeger, K. M., Helis, D. L., Huang, H. Z., Keppel, G., Kolomensky, Yu. G., Kowalski, R., Liu, R., Ma, L., Ma, Y. G., Marini, L., Maruyama, R. H., Mayer, D., Mei, Y., Moore, M. N., Napolitano, T., Nastasi, M., Nones, C., Norman, E. B., Nucciotti, A., Nutini, I., O'Donnell, T., Olmi, M., Oregui, B. T., Ouellet, J. L., Pagan, S., Pagliarone, C. E., Pagnanini, L., Pallavicini, M., Pattavina, L., Pavan, M., Pessina, G., Pettinacci, V., Pira, C., Pirro, S., Ponce, I., Pottebaum, E. G., Pozzi, S., Previtali, E., Puiu, A., Quitadamo, S., Ressa, A., Rosenfeld, C., Schmidt, B., Sharma, V., Singh, V., Sisti, M., Speller, D., Surukuchi, P. T., Taffarello, L., Tomei, C., Torres, J. A, Vetter, K. J., Vignati, M., Wagaarachchi, S. L., Welliver, B., Wilson, J., Wilson, K., Winslow, L. A., Zimmermann, S., Zucchelli, S.

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad

Externí odkaz: http://arxiv.org/abs/2405.17937

Sub-universal variational circuits for combinatorial optimization problems

Autor: Weitz, Gal, Pira, Lirandë, Ferrie, Chris, Combes, Joshua

Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research. This work introduces a novel class of classical probabilistic circuits d

Externí odkaz: http://arxiv.org/abs/2308.14981

Genetic diversity and signatures of selection in Anglo-Arabian racehorses based on medium-density genotype data

Autor: Michela Ablondi, Emanuela Pira, Vittoria Asti, Alberto Sabbioni, Maria Luisa Dettori, Giuseppe Massimo Vacca, Michele Pazzola

Publikováno v: Italian Journal of Animal Science, Vol 23, Iss 1, Pp 961-969 (2024)

The Anglo-Arabian horse breed (AA) is the result of crossing between Thoroughbred and Arabian horses renowned for versatility in show-jumping and racing. Southwest France and Sardinia in Italy are considered the historical nucleus of European AA bree

Externí odkaz: https://doaj.org/article/9c0cc97d497d414dbc4312b660650d1c

Twelve-crystal prototype of Li$_2$MoO$_4$ scintillating bolometers for CUPID and CROSS experiments

Autor: CUPID, collaborations, CROSS, Alfonso, K., Armatol, A., Augier, C., Avignone III, F. T., Azzolini, O., Balata, M., Bandac, I. C., Barabash, A. S., Bari, G., Barresi, A., Baudin, D., Bellini, F., Benato, G., Berest, V., Beretta, M., Bettelli, M., Biassoni, M., Billard, J., Boldrini, V., Branca, A., Brofferio, C., Bucci, C., Calvo-Mozota, J. M., Camilleri, J., Campani, A., Capelli, C., Capelli, S., Cappelli, L., Cardani, L., Carniti, P., Casali, N., Celi, E., Chang, C., Chiesa, D., Clemenza, M., Colantoni, I., Copello, S., Craft, E., Cremonesi, O., Creswick, R. J., Cruciani, A., D'Addabbo, A., D'Imperio, G., Dabagov, S., Dafinei, I., Danevich, F. A., De Jesus, M., de Marcillac, P., Dell'Oro, S., Di Domizio, S., Di Lorenzo, S., Dixon, T., Dompé, V., Drobizhev, A., Dumoulin, L., Fantini, G., Faverzani, M., Ferri, E., Ferri, F., Ferroni, F., Figueroa-Feliciano, E., Foggetta, L., Formaggio, J., Franceschi, A., Fu, C., Fu, S., Fujikawa, B. K., Gallas, A., Gascon, J., Ghislandi, S., Giachero, A., Gianvecchio, A., Girola, M., Gironi, L., Giuliani, A., Gorla, P., Gotti, C., Grant, C., Gras, P., Guillaumon, P. V., Gutierrez, T. D., Han, K., Hansen, E. V., Heeger, K. M., Helis, D. L., Huang, H. Z., Ianni, A., Imbert, L., Johnston, J., Juillard, A., Karapetrov, G., Keppel, G., Khalife, H., Kobychev, V. V., Kolomensky, Yu. G., Konovalov, S. I., Kowalski, R., Langford, T., Lefevre, M., Liu, R., Liu, Y., Loaiza, P., Ma, L., Madhukuttan, M., Mancarella, F., Marrache-Kikuchi, C. A., Marini, L., Marnieros, S., Martinez, M., Maruyama, R. H., Mas, Ph., Mayer, D., Mazzitelli, G., Mei, Y., Milana, S., Morganti, S., Napolitano, T., Nastasi, M., Nikkel, J., Nisi, S., Nones, C., Norman, E. B., Novosad, V., Nutini, I., O'Donnell, T., Olivieri, E., Olmi, M., Ouellet, J. L., Pagan, S., Pagliarone, C., Pagnanini, L., Pattavina, L., Pavan, M., Peng, H., Pessina, G., Pettinacci, V., Pira, C., Pirro, S., Poda, D. V., Polischuk, O. G., Ponce, I., Pozzi, S., Previtali, E., Puiu, A., Quitadamo, S., Ressa, A., Rizzoli, R., Rosenfeld, C., Rosier, P., Scarpaci, J. A., Schmidt, B., Sharma, V., Shlegel, V. N., Singh, V., Sisti, M., Slocum, P., Speller, D., Surukuchi, P. T., Taffarello, L., Tomei, C., Torres, J. A., Tretyak, V. I., Tsymbaliuk, A., Velazquez, M., Vetter, K. J., Wagaarachchi, S. L., Wang, G., Wang, L., Wang, R., Welliver, B., Wilson, J., Wilson, K., Winslow, L. A., Xue, M., Yan, L., Yang, J., Yefremenko, V., Umatov, V. I., Zarytskyy, M. M., Zhang, J., Zolotarova, A., Zucchelli, S.

An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, was constructed and tested i

Externí odkaz: http://arxiv.org/abs/2304.04611