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Ground state representations with artificial neural network methods enable high-accuracy simulations of quantum many-body systems. The authors study the performance of the transformer network architecture on this task and demonstrate its vast potential for novel findings in quantum physics.
Understanding the physics that govern the dynamics of liquid droplets and their interaction with solid surfaces is crucial for a range of industrial applications, from pesticides to solar cells. Here, the authors develop a framework based on the Young-Laplace equation, to predict the force required to detach a drop from flat and microstructured solid surfaces.
Developing cryogenic memory is a crucial undertaking in advancing superconducting logic systems. Here, the authors demonstrate the realization of a superconducting memory cell, whose state is encoded by the number of current vortices within a Josephson junction, and the readout employs microwave currents for an energy-efficient, non-destructive process.
The distinct near- and far-field directionalities of three fundamental dipoles (electric, Huygens and Janus) can be used to manipulate electromagnetic waves. The authors juxtapose the properties for these dipoles and propose a realistic active Janus source which suppresses mutual coupling by close to 1000-fold for two closely-spaced antennas.
Quantitative models for real-world epidemics are typically memory-dependent, but this presents difficulties for data collection and computation, while such difficulties do not arise in traditional Markovian models. The authors develop a framework to study the conditions under which memory-dependent processes can be modeled as Markovian frameworks.
Typically, mJ lasers generate 50 keV temperature plasma electrons. Here, the authors use mJ laser pulses to chisel a liquid droplet surface and generate an electron temperature of 1 MeV, a feat previously possible only with 100 times more powerful lasers.
High-quality measurements with good statistics, especially in a single shot, require fluxes and energies beyond the current capabilities of laserbased betatron x-ray sources. Here, the authors propose a method to enhance the flux and brightness of such sources without increasing the laser energy, paving the way for single-shot x-ray measurements in ultrafast science.
There is no universal way of optimizing the variation quantum circuits used in Noisy Intermediate-Scale Quantum (NISQ) applications. In this paper the authors introduce a new classical Bayesian optimizer, which converges much more quickly than conventional approaches, and test it for solving the Quantum Approximate Optimization Algorithm (QAOA) problem.
There has been great success in observing the spontaneous symmetry breaking (SSB) of temporal cavity solitons (TCS) in Kerr ring resonators, but similar phenomena in linear Fabry-Pérot cavities are still unexplored. The authors establish the field polarization properties for the SSB of TCS, and characterize the SSB in a model Fabry-Perot resonator.
Generating stable frequency combs with desired features is crucial for enabling applications in diverse fields, such as telecommunications, spectroscopy, and artificial intelligence. In this work, the authors demonstrated an autonomous optimization scheme based on genetic algorithms to tailor coherent microcombs produced by a microring resonator.
The long term and persistent challenges faced by women and other minorities in science requires dedicated strategies. Here the authors share the example of “Parité sciences”, game changer initiative deployed in Québec to address gender disparity.
The precision of the signal transduction process is fundamental for biological functions, and it builds on a trade-off between minimizing the response noise while retaining high sensitivity. The authors derive a general relationship between response noise and sensitivity in signaling systems, identifying the optimal conditions to minimize the noise.
The mechanism behind the transient emergence of superconductivity upon irradiation with light in some materials is highly debated, which is in part due to the strong correlations at play. Here, the authors investigate the dynamical emergence of superconductivity in the strongly correlated, yet exactly solvable, Yukawa-Sachdev-Ye-Kitaev model and discuss differences and similarities to the relaxation dynamics of conventional superconductors.
Women+ continue to face obstacles at each step along the way of pursuing a scientific career, and physics has one of the lowest gender diverse participation of all STEM subjects. This is a tremendous waste of potential that can only be reversed with a significant cultural change by all participants.
Urbasi Sinha is a Professor of Light and Matter Physics at the Raman Research Institute, India. Her research is in the field of quantum technologies, where she uses experimental methods to investigate quantum information processing, precision tests of quantum mechanics, photonic quantum computing as well as quantum communications including quantum key distribution (QKD) in free space, fibre and integrated photonics.
Optical intersite spin transfer (OISTR), which is driven by an ultrafast optical excitation, was recently found in several materials, but there is some disagreement over how this phenomenon can be observed experimentally. Here, the authors investigate the mechanism of intersite spin transfer in a set of FeNi alloys and make a comparison with pure Ni, demonstrating and discussing the challenges of observing OISTR using magneto-optical measurements.
Optical techniques adopted in optical computing rely on spatial multiplexing, requiring numerous integrated elements and restricting the architecture to perform a single kernel convolution per layer. The authors demonstrate a fiber-optic computing architecture based on temporal multiplexing that performs multiple convolutions in a single layer.
In this paper the authors explain the many-body non-Hermitian skin effect (NHSE) from the angle of doublon-holon pairs in the spin-full Hatano-Nelson model. The main result is that while strong interactions suppress doublon-holon pairs in the ground state, leading to the absence of the NHSE, excited eigenstates exhibit these excitations, with doublons and holons moving toward opposite directions.
To prepare steerable assembles from a bipartite quantum state is a cumbersome task due to the optimization over all possible incompatible measurements. Here the authors leverage the power of the deep learning model to infer the hierarchy of steering measurement settings and reveal the most compact parameters to characterize the Alice-to-Bob steerability.
Tanusri Saha-Dasgupta is a Professor and Director at S.N. Bose National Centre for Basic Sciences, India. Her research focuses on computational condensed matter physics and the study of the optical, electronic, and magnetic properties of materials from first principles. Tanusri has been widely engaged in working groups, meetings, and other activities to promote gender parity in Indian academic institutions.