Department of Physics

Abstract: The application of resonant magnetic perturbation (RMP) with toroidal mode number n = 1 on HL-3 tokamak inhibits the L-H transition at specific heating power. Following RMP application, the electron density increases in the outer plasma region (ρ > 0.85, where ρ is the normalized toroidal flux), while the electron/ion temperature decreases. Notably, the equilibrium flow shear in the edge region is substantially reduced. This reduction, combined with enhanced micro-instabilities driven by increased profile gradients, leads to enhanced turbulence levels. Consequently, the diminished flow shear becomes less effective in suppressing turbulence, providing a comprehensive explanation for the inhibited access to H-mode. Through a modified one-dimensional predator–prey model that incorporates the effects of RMP-induced radial magnetic perturbations, we have conducted a quantitative analysis of the turbulence and flow dynamics during the L-H transition process. Our results indicate that as the strength of magnetic perturbation increases, the turbulence intensity increases and edge flow shear decreases, in agreement with experimental observations. Additionally, we found that the L-H transition power threshold increases almost linearly with the square of the radial magnetic perturbation intensity. These results enhance our understanding of RMP-induced changes in edge plasma transport, providing valuable insights for optimizing the operation of future tokamaks and improving the performance of fusion reactors.

Abstract: This study addresses key aspects of momentum transport in hydrodynamic disks, which is critical for understanding zonal flow generation and turbulence in compressible hydrodynamic disks. We find that nonlinear momentum/density transport leads to the formation of zonal flows from the Rossby wave instability in disks. We analytically derive the generation and location of zonal flows and describe a modified Taylor identity applicable to compressible disk flows. We further present a self-regulation model, revealing a dynamic interplay between zonal flow and fluctuations driven by Rossby wave instability that regulates the nonlinear saturation state. This theoretical framework contributes insights into the dynamics of disks such as protoplanetary disks, shedding light on the intricate processes governing momentum/density transport and the emergence of zonal flows in the saturation of protoplanetary disks.

Extracting information efficiently from quantum systems is crucial for quantum information processing. Classical shadows enable predicting many properties of arbitrary quantum states using few measurements. While random single-qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Introducing a shallow random quantum circuit before measurements improves sample efficiency for high-weight Pauli observables and low-rank properties. However, in practice, these circuits can be noisy and bias the measurement results. Here, we propose the robust shallow shadows, which employs Bayesian inference to learn and mitigate noise in postprocessing. We analyze noise effects on sample complexity and the optimal circuit depth. We provide theoretical guarantees for the success of error mitigation under a wide class of noise processes. Experimental validation on a superconducting quantum processor confirms the advantage of our method, even in the presence of realistic noise, over single-qubit measurements for predicting diverse state properties, such as fidelity and entanglement entropy. Our protocol thus offers a scalable, robust, and sample-efficient method for quantum state characterization on near-term quantum devices.

A correction to this paper has been published: https://doi.org/10.1007/JHEP06(2024)154

Materials that effectively separate charge and spin currents are key to advancing spin-orbit torque-based switching devices for nanomagnet memory. NiO, an insulating yet spin-conducting material, is essential in such systems. Interfacing NiO with a heavy metal like Pt, confines charge current to Pt while allowing spin current to pass through NiO into an adjacent NiFe layer. Introducing a spin-transparent Cu layer between NiO and Py prevents exchange interactions, transmits spin torque, and ensures a uniform magnetic environment at the Py interface, ensuring device reliability. To study spin-current conduction, we use dc bias-dependent spin-torque ferromagnetic resonance (ST-FMR) on nanobridges patterned from a Pt/NiO/Cu/NiFe stack with varying NiO thickness. Results show that a highly spin-transparent (93%) Cu spacer enables >40% spin-current transmission through defect-free NiO/Cu bilayers for NiO thicker than 1.5 nm. This stack demonstrates effective charge-spin separation and flexibility, with seamless spin-torque conversion from magnonic to electronic transport, enabling new spin-current-based device designs.