MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification

Autor: Agarwal, Saurabh, Arya, K. V., Meena, Yogesh Kumar

Chest X-ray imaging is a critical diagnostic tool for identifying pulmonary diseases. However, manual interpretation of these images is time-consuming and error-prone. Automated systems utilizing convolutional neural networks (CNNs) have shown promis

Externí odkaz: http://arxiv.org/abs/2401.00728

Phase transition in Random Circuit Sampling

Autor: Morvan, A., Villalonga, B., Mi, X., Mandrà, S., Bengtsson, A., Klimov, P. V., Chen, Z., Hong, S., Erickson, C., Drozdov, I. K., Chau, J., Laun, G., Movassagh, R., Asfaw, A., Brandão, L. T. A. N., Peralta, R., Abanin, D., Acharya, R., Allen, R., Andersen, T. I., Anderson, K., Ansmann, M., Arute, F., Arya, K., Atalaya, J., Bardin, J. C., Bilmes, A., Bortoli, G., Bourassa, A., Bovaird, J., Brill, L., Broughton, M., Buckley, B. B., Buell, D. A., Burger, T., Burkett, B., Bushnell, N., Campero, J., Chang, H. S., Chiaro, B., Chik, D., Chou, C., Cogan, J., Collins, R., Conner, P., Courtney, W., Crook, A. L., Curtin, B., Debroy, D. M., Barba, A. Del Toro, Demura, S., Di Paolo, A., Dunsworth, A., Faoro, L., Farhi, E., Fatemi, R., Ferreira, V. S., Burgos, L. Flores, Forati, E., Fowler, A. G., Foxen, B., Garcia, G., Genois, E., Giang, W., Gidney, C., Gilboa, D., Giustina, M., Gosula, R., Dau, A. Grajales, Gross, J. A., Habegger, S., Hamilton, M. C., Hansen, M., Harrigan, M. P., Harrington, S. D., Heu, P., Hoffmann, M. R., Huang, T., Huff, A., Huggins, W. J., Ioffe, L. B., Isakov, S. V., Iveland, J., Jeffrey, E., Jiang, Z., Jones, C., Juhas, P., Kafri, D., Khattar, T., Khezri, M., Kieferová, M., Kim, S., Kitaev, A., Klots, A. R., Korotkov, A. N., Kostritsa, F., Kreikebaum, J. M., Landhuis, D., Laptev, P., Lau, K. -M., Laws, L., Lee, J., Lee, K. W., Lensky, Y. D., Lester, B. J., Lill, A. T., Liu, W., Livingston, W. P., Locharla, A., Malone, F. D., Martin, O., Martin, S., McClean, J. R., McEwen, M., Miao, K. C., Mieszala, A., Montazeri, S., Mruczkiewicz, W., Naaman, O., Neeley, M., Neill, C., Nersisyan, A., Newman, M., Ng, J. H., Nguyen, A., Nguyen, M., Niu, M. Yuezhen, O'Brien, T. E., Omonije, S., Opremcak, A., Petukhov, A., Potter, R., Pryadko, L. P., Quintana, C., Rhodes, D. M., Rosenberg, E., Rocque, C., Roushan, P., Rubin, N. C., Saei, N., Sank, D., Sankaragomathi, K., Satzinger, K. J., Schurkus, H. F., Schuster, C., Shearn, M. J., Shorter, A., Shutty, N., Shvarts, V., Sivak, V., Skruzny, J., Smith, W. C., Somma, R. D., Sterling, G., Strain, D., Szalay, M., Thor, D., Torres, A., Vidal, G., Heidweiller, C. Vollgraff, White, T., Woo, B. W. K., Xing, C., Yao, Z. J., Yeh, P., Yoo, J., Young, G., Zalcman, A., Zhang, Y., Zhu, N., Zobrist, N., Rieffel, E. G., Biswas, R., Babbush, R., Bacon, D., Hilton, J., Lucero, E., Neven, H., Megrant, A., Kelly, J., Aleiner, I., Smelyanskiy, V., Kechedzhi, K., Chen, Y., Boixo, S.

Publikováno v: Nature 634, 328-333 (2024)

Undesired coupling to the surrounding environment destroys long-range correlations on quantum processors and hinders the coherent evolution in the nominally available computational space. This incoherent noise is an outstanding challenge to fully lev

Externí odkaz: http://arxiv.org/abs/2304.11119

Physics Informed Extreme Learning Machines With Residual Variation Diminishing Scheme for Nonlinear Problems With Discontinuous Surfaces

Autor: Kaumudi Joshi, Vukka Snigdha, Arya K. Bhattacharya

Publikováno v: IEEE Access, Vol 12, Pp 130617-130629 (2024)

This work extends Extreme Learning Machines (ELM) to obtain solutions for nonlinear higher order partial differential equations that govern the physics of different domains. The ELM operates in sync with a novel Residual Variation Diminishing Scheme

Externí odkaz: https://doaj.org/article/44497e6a59384ae19aa3ec582b541247

Removing leakage-induced correlated errors in superconducting quantum error correction

Autor: McEwen, M., Kafri, D., Chen, Z., Atalaya, J., Satzinger, K. J., Quintana, C., Klimov, P. V., Sank, D., Gidney, C., Fowler, A. G., Arute, F., Arya, K., Buckley, B., Burkett, B., Bushnell, N., Chiaro, B., Collins, R., Demura, S., Dunsworth, A., Erickson, C., Foxen, B., Giustina, M., Huang, T., Hong, S., Jeffrey, E., Kim, S., Kechedzhi, K., Kostritsa, F., Laptev, P., Megrant, A., Mi, X., Mutus, J., Naaman, O., Neeley, M., Neill, C., Niu, M., Paler, A., Redd, N., Roushan, P., White, T. C., Yao, J., Yeh, P., Zalcman, A., Chen, Yu, Smelyanskiy, V. N., Martinis, John M., Neven, H., Kelly, J., Korotkov, A. N., Petukhov, A. G., Barends, R.

Publikováno v: Nat Commun 12, 1761 (2021)

Quantum computing can become scalable through error correction, but logical error rates only decrease with system size when physical errors are sufficiently uncorrelated. During computation, unused high energy levels of the qubits can become excited,

Externí odkaz: http://arxiv.org/abs/2102.06131

Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms

Autor: Foxen, B., Neill, C., Dunsworth, A., Roushan, P., Chiaro, B., Megrant, A., Kelly, J., Chen, Zijun, Satzinger, K., Barends, R., Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Boixo, S., Buell, D., Burkett, B., Chen, Yu, Collins, R., Farhi, E., Fowler, A., Gidney, C., Giustina, M., Graff, R., Harrigan, M., Huang, T., Isakov, S. V., Jeffrey, E., Jiang, Z., Kafri, D., Kechedzhi, K., Klimov, P., Korotkov, A., Kostritsa, F., Landhuis, D., Lucero, E., McClean, J., McEwen, M., Mi, X., Mohseni, M., Mutus, J. Y., Naaman, O., Neeley, M., Niu, M., Petukhov, A., Quintana, C., Rubin, N., Sank, D., Smelyanskiy, V., Vainsencher, A., White, T. C., Yao, Z., Yeh, P., Zalcman, A., Neven, H., Martinis, John M.

Publikováno v: Phys. Rev. Lett. 125, 120504 (2020)

Quantum algorithms offer a dramatic speedup for computational problems in machine learning, material science, and chemistry. However, any near-term realizations of these algorithms will need to be heavily optimized to fit within the finite resources

Externí odkaz: http://arxiv.org/abs/2001.08343