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G. S. Paraoanu
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Recent experiments with superconducting qubits are motivated by the goal of fabricating a quantum computer, but at the same time they illuminate the more fundamental aspects of quantum mechanics. In this paper we analyze the physics of switching current measurements from the point of view of macroscopic quantum mechanics.
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A new technique was recently developed to approximate the solution of the Schroedinger equation. This approximation (dubbed ERS) is shown to yield a better accuracy than the WKB-approximation. Here, we review the ERS approximation and its application to one and three-dimensional systems. In particular, we treat bound state solutions. We further focus on random potentials in a quantum wire and discuss the solution in the context of Anderson localization.
The energies of valley-orbit states in silicon quantum dots are determined by an as yet poorly understood interplay between interface roughness, orbital confinement, and electron interactions. Here, we report measurements of one- and two-electron valley-orbit state energies as the dot potential is modified by changing gate voltages, and we calculate these same energies using full configuration interaction calculations. The results enable an understanding of the interplay between the physical contributions and enable a new probe of the quantum well interface.
We develop a physical model for how galactic disks survive and/or are destroyed in interactions. Based on dynamical arguments, we show gas primarily loses angular momentum to internal torques in a merger. Gas within some characteristic radius (a function of the orbital parameters, mass ratio, and gas fraction of the merging galaxies), will quickly lose angular momentum to the stars sharing the perturbed disk, fall to the center and be consumed in a starburst. A similar analysis predicts where violent relaxation of the stellar disks is efficient. Our model allows us to predict the stellar and gas content that will survive to re-form a disk in the remnant, versus being violently relaxed or contributing to a starburst. We test this in hydrodynamic simulations and find good agreement as a function of mass ratio, orbital parameters, and gas fraction, in simulations spanning a wide range in these properties and others, including different prescriptions for gas physics and feedback. In an immediate sense, the amount of disk that re-forms can be understood in terms of well-understood gravitational physics, independent of details of ISM gas physics or feedback. This allows us to explicitly quantify the requirements for such feedback to (indirectly) enable disk survival, by changing the pre-merger gas content and distribution. The efficiency of disk destruction is a strong function of gas content: we show how and why sufficiently gas-rich major mergers can, under general conditions, yield systems with small bulges (B/T<0.2). We provide prescriptions for inclusion of our results in semi-analytic models.
Approximately 10 per cent of star clusters are found in pairs, known as binary clusters. We propose a mechanism for binary cluster formation; we use N-body simulations to show that velocity substructure in a single (even fairly smooth) region can cause binary clusters to form. This process is highly stochastic and it is not obvious from a regions initial conditions whether a binary will form and, if it does, which stars will end up in which cluster. We find the probability that a region will divide is mainly determined by its virial ratio, and a virial ratio above equilibrium is generally necessary for binary formation. We also find that the mass ratio of the two clusters is strongly influenced by the initial degree of spatial substructure in the region.
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system. We provide a basic overview of physics-based learning, the construction of a physics-based network, and its reduction to practice. Specifically, we advocate exploiting the auto-differentiation functionality twice, once to build a physics-based network and again to perform physics-based learning. Thus, the user need only implement the forward model process for their system, speeding up prototyping time. We provide an open-source Pytorch implementation of a physics-based network and training procedure for a generic sparse recovery problem
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