A robust boson dispenser: Quantum state preparation in interacting many-particle systems

We present a technique to control the spatial state of a small cloud of interacting particles at low temperatures with almost perfect fidelity using spatial adiabatic passage. To achieve this, the resonant trap energies of the system are engineered in such a way that a single, well-defined eigenstate connects the initial and desired states and is isolated from the rest of the spectrum. We apply this procedure to the task of separating a well-defined number of particles from an initial cloud and show that it can be implemented in radio-frequency traps using experimentally realistic parameters.Comment: 10 pages, 9 figure

State engineering in one-dimensional quantum gases

The development of quantum technologies requires the understanding, controlling and engineering of quantum states of interacting systems, a challenge currently driven by experimental progress. In this work I study, both analytically and numerically, two specific models of one-dimensional ultracold atomic systems to determine their states and accessible dynamical behaviour. The first part of the work deals with the creation of a bosonic atom dispenser, a tool which would allow to deterministically separate any number of atoms from an interacting ultracold gas or create a many-particle noon state. By engineering an effectively three-level system, I show that a robust adiabatic process exists that connects the initial and target Fock states. Moreover, I demonstrate its potential to be experimentally implemented using radio-frequency traps. In the second part, I derive an analytical single-particle solution for the arbitrary finite Kronig–Penney model. In this model the atoms are trapped in an infinite square well which contains an arbitrary number of arbitrarily positioned point-like barriers of arbitrary heights. I also demonstrate that using certain parameters in the model as extra (virtual) dimensions one can observe the emergence of higher-dimensional physics in this one-dimensional system. In particular, I show the appearance of edge states and the emergence of a Hofstadter butterfly-like momentum spectrum in various configurations of the model. Finally, using the single-particle solutions, I study many-body correlations in a gas of either infinitely repulsive bosons or non-interacting fermions.Okinawa Institute of Science and Technology Graduate Universit

Entanglement in spatial adiabatic processes for interacting atoms

We study the dynamics of the non-classical correlations for few atom systems in the presence of strong interactions for a number of recently developed adiabatic state preparation protocols. We show that entanglement can be created in a controlled fashion and can be attributed to two distinct sources, the atom-atom interaction and the distribution of atoms among different traps.Comment: 9 pages, 3 figure

The Virtual Driving Instructor: Multi-Agent System Collaborating via Knowledge Graph for Scalable Driver Education

This paper introduces the design, development, and deployment of a Virtual Driving Instructor (VDI) for enhanced driver education. The VDI provides personalized, real-time feedback to students in a driving simulator, addressing some of the limitations of traditional driver instruction. Employing a hybrid AI system, the VDI combines rule-based agents, learning-based agents, knowledge graphs, and Bayesian networks to assess and monitor student performance in a comprehensive manner. Implemented in multiple simulators at a driving school in Norway, the system aims to leverage AI and driving simulation to improve both the learning experience and the efficiency of instruction. Initial feedback from students has been largely positive, highlighting the effectiveness of this integration while also pointing to areas for further improvement. This work marks a significant stride in infusing technology into driver education, offering a scalable and efficient approach to instruction

A Virtual Driving Instructor That Generates Personalized Driving Lessons Based on Student Skill Level

Currently, students acquire driving skills by practicing in actual traffic conditions and through direct interactions with an instructor. While one-on-one interactions could be tailored to a student’s learning style and skill level, making them effective for learning, one-on-one interactions are also inefficient, potentially costly, and not standardized with limitations on which traffic situation can be safely taught. For these exact reasons Way AS has developed and commercially deployed a virtual driving instructor that educates students in high-fidelity simulators. In this paper, we present a module, the Lesson generator, that extends the virtual driving instructor to generate personalized lessons for individual students with the goal to practice in a focused and deliberately fashion the skills that need practice for the students to become proficient drivers. A case study is presented, and the path to deployment is discussed