EPIC: Enhancing Privacy through Iterative Collaboration

Autor: Chourasia, Prakash, Lonkar, Heramb, Ali, Sarwan, Patterson, Murray

Advancements in genomics technology lead to a rising volume of viral (e.g., SARS-CoV-2) sequence data, resulting in increased usage of machine learning (ML) in bioinformatics. Traditional ML techniques require centralized data collection and processi

Externí odkaz: http://arxiv.org/abs/2411.05167

Measurement Schemes for Quantum Linear Equation Solvers

Autor: Patterson, Andrew, Lapworth, Leigh

Solving Computational Fluid Dynamics (CFD) problems requires the inversion of a linear system of equations, which can be done using a quantum algorithm for matrix inversion arxiv:1806.01838. However, the number of shots required to measure the output

Externí odkaz: http://arxiv.org/abs/2411.00723

MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering

Autor: Ali, Sarwan, Chourasia, Prakash, Mansoor, Haris, koirala, Bipin, Patterson, Murray

The t-Distributed Stochastic Neighbor Embedding (t-SNE) has emerged as a popular dimensionality reduction technique for visualizing high-dimensional data. It computes pairwise similarities between data points by default using an RBF kernel and random

Externí odkaz: http://arxiv.org/abs/2410.15688

Measurement of the double-differential cross section of muon-neutrino charged-current interactions with low hadronic energy in the NOvA Near Detector

Autor: Acero, M. A., Acharya, B., Adamson, P., Aliaga, L., Anfimov, N., Antoshkin, A., Arrieta-Diaz, E., Asquith, L., Aurisano, A., Back, A., Balashov, N., Baldi, P., Bambah, B. A., Bannister, E., Barros, A., Bashar, S., Bat, A., Bays, K., Bernstein, R., Bezerra, T. J. C., Bhatnagar, V., Bhattarai, D., Bhuyan, B., Bian, J., Booth, A. C., Bowles, R., Brahma, B., Bromberg, C., Buchanan, N., Butkevich, A., Calvez, S., Carroll, T. J., Catano-Mur, E., Cesar, J. P., Chatla, A., Chirco, R., Choudhary, B. C., Christensen, A., Cicala, M. F., Coan, T. E., Cooleybeck, A., Cortes-Parra, C., Coveyou, D., Cremonesi, L., Davies, G. S., Derwent, P. F., Ding, P., Djurcic, Z., Dobbs, K., Dolce, M., Doyle, D., Tonguino, D. Dueñas, Dukes, E. C., Dye, A., Ehrlich, R., Ewart, E., Filip, P., Frank, M. J., Gallagher, H. R., Gao, F., Giri, A., Gomes, R. A., Goodman, M. C., Groh, M., Group, R., Habig, A., Hakl, F., Hartnell, J., Hatcher, R., He, M., Heller, K., Hewes, V, Himmel, A., Horoho, T., Ivaneev, Y., Ivanova, A., Jargowsky, B., Jarosz, J., Johnson, C., Judah, M., Kakorin, I., Kaplan, D. M., Kalitkina, A., Kirezli-Ozdemir, B., Kleykamp, J., Klimov, O., Koerner, L. W., Kolupaeva, L., Kralik, R., Kumar, A., Kus, V., Lackey, T., Lang, K., Lesmeister, J., Lister, A., Liu, J., Lock, J. A., Lokajicek, M., MacMahon, M., Magill, S., Mann, W. A., Manoharan, M. T., Plata, M. Manrique, Marshak, M. L., Martinez-Casales, M., Matveev, V., Mehta, B., Messier, M. D., Meyer, H., Miao, T., Miller, W. H., Mishra, S., Mishra, S. R., Mislivec, A., Mohanta, R., Moren, A., Morozova, A., Mu, W., Mualem, L., Muether, M., Mulder, K., Myers, D., Naples, D., Nath, A., Nelleri, S., Nelson, J. K., Nichol, R., Niner, E., Norman, A., Norrick, A., Nosek, T., Oh, H., Olshevskiy, A., Olson, T., Ozkaynak, M., Pal, A., Paley, J., Panda, L., Patterson, R. B., Pawloski, G., Petti, R., Porter, J. C. C., Prais, L. R., Rabelhofer, M., Rafique, A., Raj, V., Rajaoalisoa, M., Ramson, B., Rebel, B., Roy, P., Samoylov, O., Sanchez, M. C., Falero, S. Sanchez, Shanahan, P., Sharma, P., Sheshukov, A., Shivam, Shmakov, A., Shorrock, W., Shukla, S., Singha, D. K., Singh, I., Singh, P., Singh, V., Smith, E., Smolik, J., Snopok, P., Solomey, N., Sousa, A., Soustruznik, K., Strait, M., Suter, L., Sutton, A., Sutton, K., Swain, S., Sweeney, C., Sztuc, A., Oregui, B. Tapia, Tas, P., Thakore, T., Thomas, J., Tiras, E., Torun, Y., Tran, D., Trokan-Tenorio, J., Urheim, J., Vahle, P., Vallari, Z., Villamil, J. D., Vockerodt, K. J., Wallbank, M., Wetstein, M., Whittington, D., Wickremasinghe, D. A., Wieber, T., Wolcott, J., Wrobel, M., Wu, S., Wu, W., Xiao, Y., Yaeggy, B., Yahaya, A., Yankelevich, A., Yonehara, K., Yu, Y., Zadorozhnyy, S., Zalesak, J., Zwaska, R.

The NOvA collaboration reports cross-section measurements for $\nu_{\mu}$ charged-current interactions with low hadronic energy (maximum kinetic energy of 250 MeV for protons and 175 MeV for pions) in the NOvA Near Detector. The results are presented

Externí odkaz: http://arxiv.org/abs/2410.10222

Demonstrating real-time and low-latency quantum error correction with superconducting qubits

Autor: Caune, Laura, Skoric, Luka, Blunt, Nick S., Ruban, Archibald, McDaniel, Jimmy, Valery, Joseph A., Patterson, Andrew D., Gramolin, Alexander V., Majaniemi, Joonas, Barnes, Kenton M., Bialas, Tomasz, Buğdaycı, Okan, Crawford, Ophelia, Gehér, György P., Krovi, Hari, Matekole, Elisha, Topal, Canberk, Poletto, Stefano, Bryant, Michael, Snyder, Kalan, Gillespie, Neil I., Jones, Glenn, Johar, Kauser, Campbell, Earl T., Hill, Alexander D.

Quantum error correction (QEC) will be essential to achieve the accuracy needed for quantum computers to realise their full potential. The field has seen promising progress with demonstrations of early QEC and real-time decoded experiments. As quantu

Externí odkaz: http://arxiv.org/abs/2410.05202