Theoretical aspects of Microwave Frequency Transport in Generic Dimensionality Semiconductors

The present work can be classified as an investigation for the theoretical study of semiconductors in the microwave (?w) frequency domain. This range owns two properties that let it to be a hot subject for the next years: from one side, there is a basic need of such a characterization to get a satisfactory description of the fundamental solid state physics; from the other side there is the urgent need to analyze and increase performances of technological products in order to improve the quality of services in human life and business. IBM announced in March 2002 the realization of the fastest silicon-based transistor, by using a modified design of a heterojunction bipolar transistor (HBT) and SiGe technology, working at a speed of 210 GHz. New methods to allow contactless measurements techniques of the transport properties of semiconductors are needed for a ?w characterization to cover the whole range

Coherent Transport of Quantum States by Deep Reinforcement Learning

Some problems in physics can be handled only after a suitable \textit{ansatz }solution has been guessed. Such method is therefore resilient to generalization, resulting of limited scope. The coherent transport by adiabatic passage of a quantum state through an array of semiconductor quantum dots provides a par excellence example of such approach, where it is necessary to introduce its so called counter-intuitive control gate ansatz pulse sequence. Instead, deep reinforcement learning technique has proven to be able to solve very complex sequential decision-making problems involving competition between short-term and long-term rewards, despite a lack of prior knowledge. We show that in the above problem deep reinforcement learning discovers control sequences outperforming the \textit{ansatz} counter-intuitive sequence. Even more interesting, it discovers novel strategies when realistic disturbances affect the ideal system, with better speed and fidelity when energy detuning between the ground states of quantum dots or dephasing are added to the master equation, also mitigating the effects of losses. This method enables online update of realistic systems as the policy convergence is boosted by exploiting the prior knowledge when available. Deep reinforcement learning proves effective to control dynamics of quantum states, and more generally it applies whenever an ansatz solution is unknown or insufficient to effectively treat the problem.Comment: 5 figure

Narrow filtered DPSK implements order-1 CAPS optical line coding

A novel family of optical line codes has been presented elsewhere, here referred to as combined amplitude-phase shift (CAPS) codes. We show here that narrow filtering of a differential phase shift keying signal with bandwidth equal to about 2/3 of the bit rate turns out to closely implement the order-1 CAPS line coding. Performance of the two systems is compared for various types of optical filters

Measuring the Temperature of a Mesoscopic Quantum Electron System by means of Single Electron Statistics

We measure the temperature of a mesoscopic system consisting of an ultra-dilute two dimensional electron gas at the $Si/SiO_2$ interface in a metal-oxide-semiconductor field effect transistor (MOSFET) quantum dot by means of the capture and emission of an electron in a point defect close to the interface. Contrarily to previous reports, we show that the capture and emission by point defects in Si n-MOSFETs can be temperature dependent down to 800 mK. As the finite quantum grand canonical ensemble model applies, the time domain charge fluctuation in the defect is used to determine the temperature of the few electron gas in the channel.Comment: 4 Figures (color