Faris Alsolamy and Anthony Grbic
Phys. Rev. Applied 22, 014001 (2024) - Published 1 July, 2024
Recent theoretical developments have demonstrated that the optimal field profile for coupling circular apertures within the Fresnel zone is a generalized cylindrical vector beam (CVB), composed of Bessel beams with different complex weights. However, there has not been a systematic method to generate such generalized CVBs. This study uses mode-converting metasurfaces to control the modal distribution within a cylindrical cavity to generate generalized CVBs, a milestone in the development of next-generation wireless power transfer operating in the Fresnel zone. Furthermore, this method allows exploration of CVBs that can be optimized and tailored for specific applications or functions.
Meng-Yang Liu, Fei-Yang Sun, Ze-Guo Chen, Zhen Wang, Ming-Hui Lu, and Yan-Feng Chen
Phys. Rev. Applied 22, 014025 (2024) - Published 11 July, 2024
Measuring the local density of states (LDOS) in continuous systems poses substantial challenges. By leveraging the Purcell effect in an elastic wave lattice, the authors achieve discrete, contactless measurements of the LDOS. This study further analyzes the distribution of fractional LDOS across various disclination structures. This method illuminates the exploration of bulk topology by examining LDOS localized at edges or within disclinations. The findings bear promising implications for characterizing topological phases and enhancing control of structural vibration.
Haotian Li, Liyuan Li, Rongxin Xiang, Wei Liu, Chunjie Yan, Zui Tao, Lei Zhang, and Ronghua Liu
Phys. Rev. Applied 22, 014027 (2024) - Published 11 July, 2024
Despite being formidable tools in artificial intelligence, artificial neural networks consume substantial energy during their training phase. This study introduces hardware-based artificial neural networks that utilize artificial and natural noncollinear spin textures, significantly reducing energy consumption and enhancing operational efficiency. The authors demonstrate two such spin-texture-based physical reservoirs, which exhibit robust information-processing capabilities in two nonlinear benchmark tests. Additionally, they implement a direct-feedback-alignment algorithm within hardware, further advancing the efficiency of deep neural networks.
Fabrizio Berritta, Jan A. Krzywda, Jacob Benestad, Joost van der Heijden, Federico Fedele, Saeed Fallahi, Geoffrey C. Gardner, Michael J. Manfra, Evert van Nieuwenburg, Jeroen Danon, Anasua Chatterjee, and Ferdinand Kuemmeth
Phys. Rev. Applied 22, 014033 (2024) - Published 15 July, 2024
In quantum information science, online Hamiltonian learning emerges as a promising tool to compensate for uncontrolled environmental effects, thereby enhancing qubit quality factors. Several estimation schemes have been proposed to boost learning efficiency, but experimental implementation has been hindered by hardware limitations. Here the authors perform physics-informed, adaptive Bayesian Hamiltonian estimation for a singlet-triplet spin qubit, using a quantum controller powered by a field-programmable gate array. These techniques allow for significantly faster and more accurate real-time tracking of low-frequency noise in solid-state qubits.
Max Karrer, Bernd Aichner, Katja Wurster, César Magén, Christoph Schmid, Robin Hutt, Barbora Budinská, Oleksandr V. Dobrovolskiy, Reinhold Kleiner, Wolfgang Lang, Edward Goldobin, and Dieter Koelle
Phys. Rev. Applied 22, 014043 (2024) - Published 17 July, 2024
Controlled engineering of vortex-pinning sites in cuprate superconductors is a pivotal goal in manufacturing devices based on magnetic flux quanta. This study employs focused helium-ion beams to create ultradense hexagonal arrays of defects in YBaCuO thin films, achieving lattice spacings as small as 20 nm. Efficient pinning by a remarkably high matching field of 6 T is observed from the critical temperature down to 2 K. This research expands the range of temperatures and magnetic fields for exploring vortex matter using regular artificial vortex-pinning landscapes.
Liam A. Pocher, Temitayo N. Adeyeye, Sidra Gibeault, Philippe Talatchian, Ursula Ebels, Daniel P. Lathrop, Jabez J. McClelland, Mark D. Stiles, Advait Madhavan, and Matthew W. Daniels
Phys. Rev. Applied 22, 014057 (2024) - Published 23 July, 2024
Superparamagnetic tunnel junctions (SMTJs) are fundamental elements of many proposed probabilistic computers, but models often fail to capture important statistical features of experimental devices. In particular, the most probable states of real devices are often not the fully magnetized states. The authors develop an efficient, measurement-driven model that agrees with measurements that were not used in the modeling process, including the power-law behavior of dwell-time distributions at subnanosecond timescales. These results open avenues to tackle challenges in modeling high-speed SMTJ circuitry.
Philip Schmidt, Remi Claessen, Gerard Higgins, Joachim Hofer, Jannek J. Hansen, Peter Asenbaum, Martin Zemlicka, Kevin Uhl, Reinhold Kleiner, Rudolf Gross, Hans Huebl, Michael Trupke, and Markus Aspelmeyer
Phys. Rev. Applied 22, 014078 (2024) - Published 30 July, 2024
The authors implement a scheme for sensing magnetic fields using a remotely located dc SQUID embedded in a microwave resonator. This configuration provides a path toward quantum-limited detection of microwave photons. The detector is used to resolve precisely the motion of a magnetically levitated superconducting microsphere. In addition to advancing magnetic field sensing at ultralow temperatures, this innovative platform has the potential to generate and measure nonclassical mechanical states of microgram-scale masses.
A. López, J.D. Costa, T. Böhnert, P.P. Freitas, R. Ferreira, I. Barbero, J. Camarero, C. León, J. Grollier, and M. Romera
Phys. Rev. Applied 22, 014082 (2024) - Published 31 July, 2024
A promising branch of neuromorphic computing aims to perform cognitive operations in hardware, leveraging the physics of efficient and well-established nanodevices. This work presents a reconfigurable classifier, based on a network of magnetic tunnel junctions, that can learn to classify spoken vowels. In this task the hardware network surpasses multilayered software neural networks with the same number of trained parameters. These results, obtained using the same devices and working principle employed in industrial spin-transfer-torque magnetic random-access memory, constitute an important step toward the development of large-scale neuromorphic networks based on established technology.
A.H. Tavabi, P. Rosi, R.B.G. Ravelli, A. Gijsbers, E. Rotunno, T. Guner, Y. Zhang, A. Roncaglia, L. Belsito, G. Pozzi, T. Denneulin, G.C. Gazzadi, M. Ghosh, R. Nijland, S. Frabboni, P.J. Peters, E. Karimi, P. Tiemeijer, R.E. Dunin-Borkowski, and V. Grillo
Phys. Rev. Applied 22, 014083 (2024) - Published 31 July, 2024
Chirality can appear at many length scales in nature. In this study the authors introduce planar chirality as a quantitative geometric measure of chirality for two-dimensional objects. They apply this measure to evaluate the chirality of nanometer-sized structures with an electron microscope. They employ an innovative electron-optics device, the orbital-angular-momentum sorter, which applies a log-polar conformal mapping to the electron wave function and reaches near-optimal resolution in orbital angular momentum.
Xiyin Ye, Ke Xia, Gerrit E. W. Bauer, and Tao Yu
Phys. Rev. Applied 22, L011001 (2024) - Published 30 July, 2024
According to textbooks, the transmission of a (quasi)particle through or over a potential barrier is symmetric, i.e. the same whether incident from the right or left. The authors predict that the conventional wisdom does not hold for magnon transistors with metallic gates, because the chiral damping due to eddy currents asymmetrically suppresses the reflection of magnons by obstacles. Nonreciprocal spin transport based on this effect could be interesting for applications, and the predictions here might be valid for other chiral objects as well.
Faris Alsolamy and Anthony Grbic
Phys. Rev. Applied 22, 014001 (2024) - Published 1 July, 2024
Recent theoretical developments have demonstrated that the optimal field profile for coupling circular apertures within the Fresnel zone is a generalized cylindrical vector beam (CVB), composed of Bessel beams with different complex weights. However, there has not been a systematic method to generate such generalized CVBs. This study uses mode-converting metasurfaces to control the modal distribution within a cylindrical cavity to generate generalized CVBs, a milestone in the development of next-generation wireless power transfer operating in the Fresnel zone. Furthermore, this method allows exploration of CVBs that can be optimized and tailored for specific applications or functions.
L.M. Rushton, L.M. Ellis, J.D. Zipfel, P. Bevington, and W. Chalupczak
Phys. Rev. Applied 22, 014002 (2024) - Published 1 July, 2024
Najmeh Eshaqi-Sani, Stefano Zippilli, and David Vitali
Phys. Rev. Applied 22, 014003 (2024) - Published 1 July, 2024
P. Bodewei, E. Şaşıoğlu, N.F. Hinsche, and I. Mertig
Phys. Rev. Applied 22, 014004 (2024) - Published 1 July, 2024
C. Kiehl, T.S. Menon, D.P. Hewatt, S. Knappe, T. Thiele, and C.A. Regal
Phys. Rev. Applied 22, 014005 (2024) - Published 2 July, 2024
Jonathan A. Gross, Clément Godfrin, Alexandre Blais, and Eva Dupont-Ferrier
Phys. Rev. Applied 22, 014006 (2024) - Published 2 July, 2024
Brandon P. Ruzic, Matthew N.H. Chow, Ashlyn D. Burch, Daniel S. Lobser, Melissa C. Revelle, Joshua M. Wilson, Christopher G. Yale, and Susan M. Clark
Phys. Rev. Applied 22, 014007 (2024) - Published 2 July, 2024
Javier Tiffenberg, Daniel Egaña-Ugrinovic, Miguel Sofo Haro, Peizhi Du, Rouven Essig, Guillermo Fernandez-Moroni, and Sho Uemura
Phys. Rev. Applied 22, 014008 (2024) - Published 2 July, 2024
Jasvith Raj Basani, Mikkel Heuck, Dirk R. Englund, and Stefan Krastanov
Phys. Rev. Applied 22, 014009 (2024) - Published 3 July, 2024
Cheng-Zhen Wang, Ulrich Kuhl, Adin Dowling, Holger Schanz, and Tsampikos Kottos
Phys. Rev. Applied 22, 014010 (2024) - Published 3 July, 2024
Alexander J. Gerra, Courtney C. Jones, Sam Dillavou, Jesse M. Hanlan, Julia Radzio, Paulo E. Arratia, and Douglas J. Durian
Phys. Rev. Applied 22, 014011 (2024) - Published 3 July, 2024
Jesse M. Hanlan and Douglas J. Durian
Phys. Rev. Applied 22, 014012 (2024) - Published 3 July, 2024
Ranjan Kumar Patel, Yifan Yuan, Ravindra Singh Bisht, Ivan Seskar, Narayan Mandayam, and Shriram Ramanathan
Phys. Rev. Applied 22, 014013 (2024) - Published 8 July, 2024
Saleh Bunaiyan, Supriyo Datta, and Kerem Y. Camsari
Phys. Rev. Applied 22, 014014 (2024) - Published 8 July, 2024
Siddhant Midha, Madhur Parashar, Anuj Bathla, David A. Broadway, Jean-Philippe Tetienne, and Kasturi Saha
Phys. Rev. Applied 22, 014015 (2024) - Published 8 July, 2024
Colum M. O’Leary, Haozhi Sha, Jianhua Zhang, Cong Su, Salman Kahn, Huaidong Jiang, Alex Zettl, Jim Ciston, and Jianwei Miao
Phys. Rev. Applied 22, 014016 (2024) - Published 8 July, 2024
Jie Yang, Baochun Wu, Sichun Zhao, Shiqi Liu, Jing Lu, Shunfang Li, and Jinbo Yang
Phys. Rev. Applied 22, 014017 (2024) - Published 9 July, 2024
Sangbin Lee, Jongheon Lee, Ada S.Y. Poon, and Sanghoek Kim
Phys. Rev. Applied 22, 014018 (2024) - Published 9 July, 2024
S.D. Baranovskii, A.V. Nenashev, D. Hertel, K. Meerholz, and F. Gebhard
Phys. Rev. Applied 22, 014019 (2024) - Published 9 July, 2024
Ren-Hui Chen, Su-Jian Niu, Zheng Ge, Zhao-Qi-Zhi Han, Xiao-Hua Wang, Hai Jiang, Zhi-Yuan Zhou, and Bao-Sen Shi
Phys. Rev. Applied 22, 014020 (2024) - Published 9 July, 2024
Carl Willem Rischau, Stefano Gariglio, Jean-Marc Triscone, and Javier del Valle
Phys. Rev. Applied 22, 014021 (2024) - Published 10 July, 2024
Xiangen Liu, Zhonghua Shen, and Alexey M. Lomonosov
Phys. Rev. Applied 22, 014022 (2024) - Published 10 July, 2024
Xiao Wang, Pengxiang Cheng, Huaijian Chen, Shupeng Zhao, Guangdong Ma, Yongchang Zhang, Pei Zhang, Hong Gao, Ruifeng Liu, and Fuli Li
Phys. Rev. Applied 22, 014023 (2024) - Published 10 July, 2024
Timon Schapeler, Niklas Lamberty, Thomas Hummel, Fabian Schlue, Michael Stefszky, Benjamin Brecht, Christine Silberhorn, and Tim J. Bartley
Phys. Rev. Applied 22, 014024 (2024) - Published 10 July, 2024
Meng-Yang Liu, Fei-Yang Sun, Ze-Guo Chen, Zhen Wang, Ming-Hui Lu, and Yan-Feng Chen
Phys. Rev. Applied 22, 014025 (2024) - Published 11 July, 2024
Measuring the local density of states (LDOS) in continuous systems poses substantial challenges. By leveraging the Purcell effect in an elastic wave lattice, the authors achieve discrete, contactless measurements of the LDOS. This study further analyzes the distribution of fractional LDOS across various disclination structures. This method illuminates the exploration of bulk topology by examining LDOS localized at edges or within disclinations. The findings bear promising implications for characterizing topological phases and enhancing control of structural vibration.
Y. Li, F. Seddighi, and G. Porat
Phys. Rev. Applied 22, 014026 (2024) - Published 11 July, 2024
Haotian Li, Liyuan Li, Rongxin Xiang, Wei Liu, Chunjie Yan, Zui Tao, Lei Zhang, and Ronghua Liu
Phys. Rev. Applied 22, 014027 (2024) - Published 11 July, 2024
Despite being formidable tools in artificial intelligence, artificial neural networks consume substantial energy during their training phase. This study introduces hardware-based artificial neural networks that utilize artificial and natural noncollinear spin textures, significantly reducing energy consumption and enhancing operational efficiency. The authors demonstrate two such spin-texture-based physical reservoirs, which exhibit robust information-processing capabilities in two nonlinear benchmark tests. Additionally, they implement a direct-feedback-alignment algorithm within hardware, further advancing the efficiency of deep neural networks.
Zihan Xia, Juan Garcia-Nila, and Daniel A. Lidar
Phys. Rev. Applied 22, 014028 (2024) - Published 11 July, 2024
Wen-Kai Yu, Shuo-Fei Wang, and Ke-Qian Shang
Phys. Rev. Applied 22, 014029 (2024) - Published 12 July, 2024
Aditya Jayaraman, Andrey V. Danilov, Jonas Bylander, and Sergey E. Kubatkin
Phys. Rev. Applied 22, 014030 (2024) - Published 12 July, 2024
Kang’an Jiang, Yuhong Cao, Dehui Huang, Zhiyan Zheng, Feiyu Ren, Zhuyikang Zhao, Su Hu, Ke Chang, Xinhui Zhao, and Hui Wang
Phys. Rev. Applied 22, 014031 (2024) - Published 12 July, 2024
Hiroshi Imamura, Hiroko Arai, Rie Matsumoto, and Toshiki Yamaji
Phys. Rev. Applied 22, 014032 (2024) - Published 12 July, 2024
Fabrizio Berritta, Jan A. Krzywda, Jacob Benestad, Joost van der Heijden, Federico Fedele, Saeed Fallahi, Geoffrey C. Gardner, Michael J. Manfra, Evert van Nieuwenburg, Jeroen Danon, Anasua Chatterjee, and Ferdinand Kuemmeth
Phys. Rev. Applied 22, 014033 (2024) - Published 15 July, 2024
In quantum information science, online Hamiltonian learning emerges as a promising tool to compensate for uncontrolled environmental effects, thereby enhancing qubit quality factors. Several estimation schemes have been proposed to boost learning efficiency, but experimental implementation has been hindered by hardware limitations. Here the authors perform physics-informed, adaptive Bayesian Hamiltonian estimation for a singlet-triplet spin qubit, using a quantum controller powered by a field-programmable gate array. These techniques allow for significantly faster and more accurate real-time tracking of low-frequency noise in solid-state qubits.
Chunlei Zhang, Mun Kim, Jianbo Wang, and Can-Ming Hu
Phys. Rev. Applied 22, 014034 (2024) - Published 15 July, 2024
I. Panadero, H. Espinós, L. Tsunaki, K. Volkova, A. Tobalina, J. Casanova, P. Acedo, B. Naydenov, R. Puebla, and E. Torrontegui
Phys. Rev. Applied 22, 014035 (2024) - Published 15 July, 2024
Xiong Xu, Weifeng Xie, Fangbiao Li, Chang Niu, Min Li, and Hui Wang
Phys. Rev. Applied 22, 014036 (2024) - Published 15 July, 2024
A.A. Martyshkin, S.E. Sheshukova, F.Y. Ogrin, E.H. Lock, D.V. Romanenko, S.A. Nikitov, and A.V. Sadovnikov
Phys. Rev. Applied 22, 014037 (2024) - Published 16 July, 2024
Yifan Wang, Weizhi Yan, Nikolai Kuznetsov, Lukáš Flajšman, Huajun Qin, and Sebastiaan van Dijken
Phys. Rev. Applied 22, 014038 (2024) - Published 16 July, 2024
Felix Köster, Kazutaka Kanno, Jun Ohkubo, and Atsushi Uchida
Phys. Rev. Applied 22, 014039 (2024) - Published 16 July, 2024
Jie Ren and Zhilin Hou
Phys. Rev. Applied 22, 014040 (2024) - Published 17 July, 2024
Shmuel Lorber, Oded Zimron, Inbal Lorena Zak, Anat Milo, and Yonatan Dubi
Phys. Rev. Applied 22, 014041 (2024) - Published 17 July, 2024
S.A. Odintsov, S.E. Sheshukova, S.A. Nikitov, and A.V. Sadovnikov
Phys. Rev. Applied 22, 014042 (2024) - Published 17 July, 2024
Max Karrer, Bernd Aichner, Katja Wurster, César Magén, Christoph Schmid, Robin Hutt, Barbora Budinská, Oleksandr V. Dobrovolskiy, Reinhold Kleiner, Wolfgang Lang, Edward Goldobin, and Dieter Koelle
Phys. Rev. Applied 22, 014043 (2024) - Published 17 July, 2024
Controlled engineering of vortex-pinning sites in cuprate superconductors is a pivotal goal in manufacturing devices based on magnetic flux quanta. This study employs focused helium-ion beams to create ultradense hexagonal arrays of defects in YBaCuO thin films, achieving lattice spacings as small as 20 nm. Efficient pinning by a remarkably high matching field of 6 T is observed from the critical temperature down to 2 K. This research expands the range of temperatures and magnetic fields for exploring vortex matter using regular artificial vortex-pinning landscapes.
Alex Fontana and Ludovic Bellon
Phys. Rev. Applied 22, 014044 (2024) - Published 18 July, 2024
Jonathan J. Carter, Pascal Birckigt, Oliver Gerberding, and Sina M. Koehlenbeck
Phys. Rev. Applied 22, 014045 (2024) - Published 18 July, 2024
Hao Chen, Jian-Lan Xie, Hua-Shan Lai, Xiao-Chen Sun, Cheng He, and Yan-Feng Chen
Phys. Rev. Applied 22, 014046 (2024) - Published 18 July, 2024
Pan Li, Wencan Chen, Jiaxin Chen, Wei Luo, and Degang Zhao
Phys. Rev. Applied 22, 014047 (2024) - Published 18 July, 2024
Nicholas H. Patino, Curtis Rasmussen, and Massimo Ruzzene
Phys. Rev. Applied 22, 014048 (2024) - Published 19 July, 2024
Weijun Feng, Gongde Guo, Song Lin, and Yongzhen Xu
Phys. Rev. Applied 22, 014049 (2024) - Published 19 July, 2024
Ma Luo and Feng Wu
Phys. Rev. Applied 22, 014050 (2024) - Published 19 July, 2024
Haicai Xiao, Xinwen Fan, Yang Kang, Xiaolong Huang, Can Li, Ning Li, Chunsheng Weng, and Xudong Fan
Phys. Rev. Applied 22, 014051 (2024) - Published 19 July, 2024
Yan-Chao Lou (娄严超), Zhi-Cheng Ren (任志成), Chao Chen (陈超), Pei Wan (万佩), Wen-Zheng Zhu (朱文正), Jing Wang (王晶), Shu-Tian Xue (薛舒天), Bo-Wen Dong (董博文), Jianping Ding (丁剑平), Xi-Lin Wang (汪喜林), and Hui-Tian Wang (王慧田)
Phys. Rev. Applied 22, 014052 (2024) - Published 22 July, 2024
Xiaoxin Xie, Zhijiang Wang, Xiaoyan Liu, and Fei Liu
Phys. Rev. Applied 22, 014053 (2024) - Published 22 July, 2024
Mu Lan, Xiaofeng Wang, Linxi Xie, Dechao Meng, Rong Wang, Yu Song, Song Sun, and Su-Huai Wei
Phys. Rev. Applied 22, 014054 (2024) - Published 22 July, 2024
Xuliang Du, Yang Shen, Zipeng Wu, Bei Zeng, and Sen Yang
Phys. Rev. Applied 22, 014055 (2024) - Published 22 July, 2024
J.D. Zipfel, P. Bevington, L. Wright, W. Chalupczak, G. Quick, B. Steele, J. Nicholson, and V. Guarrera
Phys. Rev. Applied 22, 014056 (2024) - Published 23 July, 2024
Liam A. Pocher, Temitayo N. Adeyeye, Sidra Gibeault, Philippe Talatchian, Ursula Ebels, Daniel P. Lathrop, Jabez J. McClelland, Mark D. Stiles, Advait Madhavan, and Matthew W. Daniels
Phys. Rev. Applied 22, 014057 (2024) - Published 23 July, 2024
Superparamagnetic tunnel junctions (SMTJs) are fundamental elements of many proposed probabilistic computers, but models often fail to capture important statistical features of experimental devices. In particular, the most probable states of real devices are often not the fully magnetized states. The authors develop an efficient, measurement-driven model that agrees with measurements that were not used in the modeling process, including the power-law behavior of dwell-time distributions at subnanosecond timescales. These results open avenues to tackle challenges in modeling high-speed SMTJ circuitry.
Krzysztof Gawarecki, Michał Wiśniewski, Maciej Polak, Robert Kudrawiec, and Marta Gładysiewicz
Phys. Rev. Applied 22, 014058 (2024) - Published 23 July, 2024
Eyob A. Sete, Vinay Tripathi, Joseph A. Valery, Daniel Lidar, and Josh Y. Mutus
Phys. Rev. Applied 22, 014059 (2024) - Published 23 July, 2024
Tao Chen, Jia-Qi Hu, Chengxian Zhang, and Zheng-Yuan Xue
Phys. Rev. Applied 22, 014060 (2024) - Published 24 July, 2024
Yuting Zhu, Shibei Xue, Fangfang Ju, and Haidong Yuan
Phys. Rev. Applied 22, 014061 (2024) - Published 24 July, 2024
A.A. Mazanik, A.E. Botha, I.R. Rahmonov, and Yu.M. Shukrinov
Phys. Rev. Applied 22, 014062 (2024) - Published 24 July, 2024
Janka Biznárová, J.C. Rivera Hernández, Daniel Forchheimer, Jonas Bylander, David B. Haviland, and Gustav Andersson
Phys. Rev. Applied 22, 014063 (2024) - Published 24 July, 2024
Jun Luo, Biao Liu, Jun-Liang Yang, and Meng-Qiu Cai
Phys. Rev. Applied 22, 014064 (2024) - Published 25 July, 2024
Erqian Dong, Jinhu Zhang, Xiaochun Su, Zhanyuan Gao, Chen Yang, Zhenyu Wang, Nicholas X. Fang, and Yu Zhang
Phys. Rev. Applied 22, 014065 (2024) - Published 25 July, 2024
Jiaoyang Zheng, Guru Khalsa, and Jeffrey Moses
Phys. Rev. Applied 22, 014066 (2024) - Published 25 July, 2024
Duc V. Dinh and Oliver Brandt
Phys. Rev. Applied 22, 014067 (2024) - Published 25 July, 2024
Jarosław Pawłowski and Mateusz Krawczyk
Phys. Rev. Applied 22, 014068 (2024) - Published 26 July, 2024
C. Xiang, A. Rodriguez, E.R. Cardozo de Oliveira, L. Le Gratiet, I. Sagnes, M. Morassi, A. Lemaître, and N.D. Lanzillotti-Kimura
Phys. Rev. Applied 22, 014069 (2024) - Published 26 July, 2024
Vijay, Shivani Sharma, Joyee Ghosh, and Vivek Venkataraman
Phys. Rev. Applied 22, 014070 (2024) - Published 26 July, 2024
Lionel Petit, Tom Fournier, Géraldine Ballon, Cédric Robert, Delphine Lagarde, Pascal Puech, Thomas Blon, and Benjamin Lassagne
Phys. Rev. Applied 22, 014071 (2024) - Published 29 July, 2024
M. Erbe, R. Berrazouane, S. Geyer, L. Stasi, F. van der Brugge, G. Gras, M. Schmidt, A.D. Wieck, A. Ludwig, F. Bussières, and R.J. Warburton
Phys. Rev. Applied 22, 014072 (2024) - Published 29 July, 2024
Yingchao Peng, Imtiar Niloy, Megan Kam, Paolo Celli, and Paul Plucinsky
Phys. Rev. Applied 22, 014073 (2024) - Published 29 July, 2024
Yiming Huang, Xiao Yuan, Huiyuan Wang, and Yuxuan Du
Phys. Rev. Applied 22, 014074 (2024) - Published 29 July, 2024
Kyle S. Latty and Kyle C. Hartig
Phys. Rev. Applied 22, 014075 (2024) - Published 29 July, 2024
Minsoo Jang, Sergey G. Menabde, Fatemeh Kiani, Jacob T. Heiden, Vladimir A. Zenin, N. Asger Mortensen, Giulia Tagliabue, and Min Seok Jang
Phys. Rev. Applied 22, 014076 (2024) - Published 30 July, 2024
Xiaodong Yan, Xuemu Li, Zhihe Long, Zehua Peng, Jing Fu, Zhuomin Zhang, Shiyuan Liu, Ying Hong, Qi Li, Shujun Zhang, Dragan Damjanovic, and Zhengbao Yang
Phys. Rev. Applied 22, 014077 (2024) - Published 30 July, 2024
Philip Schmidt, Remi Claessen, Gerard Higgins, Joachim Hofer, Jannek J. Hansen, Peter Asenbaum, Martin Zemlicka, Kevin Uhl, Reinhold Kleiner, Rudolf Gross, Hans Huebl, Michael Trupke, and Markus Aspelmeyer
Phys. Rev. Applied 22, 014078 (2024) - Published 30 July, 2024
The authors implement a scheme for sensing magnetic fields using a remotely located dc SQUID embedded in a microwave resonator. This configuration provides a path toward quantum-limited detection of microwave photons. The detector is used to resolve precisely the motion of a magnetically levitated superconducting microsphere. In addition to advancing magnetic field sensing at ultralow temperatures, this innovative platform has the potential to generate and measure nonclassical mechanical states of microgram-scale masses.
Taryn V. Stefanski and Christian Kraglund Andersen
Phys. Rev. Applied 22, 014079 (2024) - Published 30 July, 2024
Juliang Li, Pete Barry, Tom Cecil, Marharyta Lisovenko, Volodymyr Yefremenko, Gensheng Wang, Serhii Kruhlov, Goran Karapetrov, and Clarence Chang
Phys. Rev. Applied 22, 014080 (2024) - Published 30 July, 2024
Michael T. Hatzon, Eugene N. Ivanov, Jeremy F. Bourhill, Maxim Goryachev, and Michael E. Tobar
Phys. Rev. Applied 22, 014081 (2024) - Published 30 July, 2024
A. López, J.D. Costa, T. Böhnert, P.P. Freitas, R. Ferreira, I. Barbero, J. Camarero, C. León, J. Grollier, and M. Romera
Phys. Rev. Applied 22, 014082 (2024) - Published 31 July, 2024
A promising branch of neuromorphic computing aims to perform cognitive operations in hardware, leveraging the physics of efficient and well-established nanodevices. This work presents a reconfigurable classifier, based on a network of magnetic tunnel junctions, that can learn to classify spoken vowels. In this task the hardware network surpasses multilayered software neural networks with the same number of trained parameters. These results, obtained using the same devices and working principle employed in industrial spin-transfer-torque magnetic random-access memory, constitute an important step toward the development of large-scale neuromorphic networks based on established technology.
A.H. Tavabi, P. Rosi, R.B.G. Ravelli, A. Gijsbers, E. Rotunno, T. Guner, Y. Zhang, A. Roncaglia, L. Belsito, G. Pozzi, T. Denneulin, G.C. Gazzadi, M. Ghosh, R. Nijland, S. Frabboni, P.J. Peters, E. Karimi, P. Tiemeijer, R.E. Dunin-Borkowski, and V. Grillo
Phys. Rev. Applied 22, 014083 (2024) - Published 31 July, 2024
Chirality can appear at many length scales in nature. In this study the authors introduce planar chirality as a quantitative geometric measure of chirality for two-dimensional objects. They apply this measure to evaluate the chirality of nanometer-sized structures with an electron microscope. They employ an innovative electron-optics device, the orbital-angular-momentum sorter, which applies a log-polar conformal mapping to the electron wave function and reaches near-optimal resolution in orbital angular momentum.
Kaiwen Yi, Yang Liu, Bowen Wang, Wei Xiao, Dong Sheng, Xiang Peng, and Hong Guo
Phys. Rev. Applied 22, 014084 (2024) - Published 31 July, 2024
Shaojie Hu, Kang Wang, Tai Min, and Takashi Kimura
Phys. Rev. Applied 22, 014085 (2024) - Published 31 July, 2024