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Experimental observation of the topological interface state in a one-dimensional waterborne acoustic waveguide

Licheng Luo, Liping Ye, Hailong He, Jiuyang Lu, Manzhu Ke, and Zhengyou Liu

Phys. Rev. Applied 20, 044008 (2023) - Published 4 October, 2023

Minnaert resonance in an array of two-dimensional bubbles

Camila Horvath, Yolanda Vargas-Hernández, and María Luisa Cordero

Phys. Rev. Applied 20, 044007 (2023) - Published 4 October, 2023

Operational entanglement-based quantum key distribution over 50 km of field-deployed optical fibers

Yoann Pelet, Grégory Sauder, Mathis Cohen, Laurent Labonté, Olivier Alibart, Anthony Martin, and Sébastien Tanzilli

Phys. Rev. Applied 20, 044006 (2023) - Published 3 October, 2023

Quantified effects of the laser-seeding attack in quantum key distribution

V. Lovic, D.G. Marangon, P.R. Smith, R.I. Woodward, and A.J. Shields

Phys. Rev. Applied 20, 044005 (2023) - Published 3 October, 2023

Nonlocal metasurface based on the multiport structural model

Jie Ren and Zhilin Hou

Phys. Rev. Applied 20, 044004 (2023) - Published 3 October, 2023

Image-force barrier lowering of Schottky barriers in two-dimensional materials as a function of metal contact angle

Sarah R. Evans, Emeric Deylgat, Edward Chen, and William G. Vandenberghe

Phys. Rev. Applied 20, 044003 (2023) - Published 3 October, 2023

Efficient and quantum-adaptive machine learning with fermion neural networks

Pei-Lin Zheng, Jia-Bao Wang, and Yi Zhang

Phys. Rev. Applied 20, 044002 (2023) - Published 2 October, 2023

Application of machine learning to quantum data and models has been held back by the lack of adaptability and efficiency in present-day neural networks. This study proposes fermion neural networks for quantum-adaptive machine learning with direct applications to complex quantum systems, and offers in situ analysis without preprocessing or presumption. An efficient optimization comparable to back propagation is established, enabling competitive performance in challenging machine-learning benchmarks. Fermion neural networks’ quantum properties, such as entanglement and correlation, also bring various advantages and insights.

Characterizing quantum states of light using ghost imaging

Xiaolong Dong, Dongyu Liu, Mingsheng Tian, Shuheng Liu, Yi Li, Tianxiao Hu, Qiongyi He, Haitan Xu, and Zheng Li

Phys. Rev. Applied 20, 044001 (2023) - Published 2 October, 2023

Ultimate parameters of an all-optical MX resonance in Cs in ultraweak magnetic field

M.V. Petrenko, A.S. Pazgalev, and A.K. Vershovskii

Phys. Rev. Applied 20, 034074 (2023) - Published 29 September, 2023

Sensitivity of an Antineutrino Monitor for Remote Nuclear Reactor Discovery

L. Kneale, S.T. Wilson, T. Appleyard, J. Armitage, N. Holland, and M. Malek

Phys. Rev. Applied 20, 034073 (2023) - Published 29 September, 2023

Native Conditional iswap Operation with Superconducting Artificial Atoms

Chang-Kang Hu, Jiahao Yuan, Bruno A. Veloso, Jiawei Qiu, Yuxuan Zhou, Libo Zhang, Ji Chu, Orkesh Nurbolat, Ling Hu, Jian Li, Yuan Xu, Youpeng Zhong, Song Liu, Fei Yan, Dian Tan, R. Bachelard, Alan C. Santos, C.J. Villas-Boas, and Dapeng Yu

Phys. Rev. Applied 20, 034072 (2023) - Published 29 September, 2023

Probing Topological Charge of Discrete Vortices

Vasu Dev and Vishwa Pal

Phys. Rev. Applied 20, 034071 (2023) - Published 29 September, 2023

Field-Free All-Optical Switching and Electrical Readout of Tb/Co-Based Magnetic Tunnel Junctions

D. Salomoni, Y. Peng, L. Farcis, S. Auffret, M. Hehn, G. Malinowski, S. Mangin, B. Dieny, L.D. Buda-Prejbeanu, R.C. Sousa, and I.L. Prejbeanu

Phys. Rev. Applied 20, 034070 (2023) - Published 28 September, 2023

Measurement-Device-Independent Quantum Key Distribution with Practical Spontaneous Parametric Down-Conversion Sources

Xiao-Hai Zhan, Zhen-Qiu Zhong, Shuang Wang, Zhen-Qiang Yin, Wei Chen, De-Yong He, Guang-Can Guo, and Zheng-Fu Han

Phys. Rev. Applied 20, 034069 (2023) - Published 28 September, 2023

Superradiant Scattering by a Limit Cycle

Tiemo Pedergnana and Nicolas Noiray

Phys. Rev. Applied 20, 034068 (2023) - Published 28 September, 2023

Tuning Arrays with Rays: Physics-Informed Tuning of Quantum Dot Charge States

Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, and Justyna P. Zwolak

Phys. Rev. Applied 20, 034067 (2023) - Published 28 September, 2023

Many methods to automatically tune silicon spin qubits are limited by reliability and data efficiency, which makes them less likely to be scalable. The authors demonstrate a reliable, efficient, physics-informed tuning algorithm (PIT) for navigating to a target charge configuration⏤a prerequisite to forming qubits. This tuning method combines machine learning and physical intuition with an algorithm that leverages one-dimensional scans (rays) and conventional peak-finding to navigate from a coarse, unknown device state to a desired charge occupation efficiently and effectively. PIT enables the transformation of an uncalibrated circuit to a functioning quantum processor.

Semiconductor-Quantum-Dot Modulator for Cryogenic Operation of Quantum Circuitry

M. R. Hogg, M.G. House, P. Pakkiam, and M.Y. Simmons

Phys. Rev. Applied 20, 034066 (2023) - Published 27 September, 2023

Quantum computing devices have the potential to solve problems that are beyond even the most powerful modern supercomputers. Most quantum hardware operates at temperatures near absolute zero, but controlling a quantum computer at cryogenic temperatures using signals propagating from room temperature becomes infeasible for large-scale processors. The authors explore a device that allows control signals to be generated on-chip at cryogenic temperatures, potentially mitigating this cryogenic bottleneck. Their device is fabricated in silicon using established technologies for quantum processors, providing a pathway towards a hybrid quantum-classical integrated circuit.

Characterizing Crosstalk of Superconducting Transmon Processors

Andreas Ketterer and Thomas Wellens

Phys. Rev. Applied 20, 034065 (2023) - Published 27 September, 2023

Inverse Design of Thin-Plate Elastic Wave Devices

J.R. Capers

Phys. Rev. Applied 20, 034064 (2023) - Published 27 September, 2023

Rotation Sensing of a Trapped Nanoparticle Assisted by Magnetic Field Gradient

L.-Y. Wang, J.-F. Wei, K.-F. Cui, S.-L. Su, M. Feng, L.-L. Yan, G. Chen, H.-Z. Guo, and C.-X. Shan

Phys. Rev. Applied 20, 034063 (2023) - Published 27 September, 2023

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