Ab Initio Modelling A Predictive Tool For Optimising Quantum Processor

Ab Initio Modelling A Predictive Tool For Optimising Quantum Processor Ab initio modelling of quantum dot qubits is a method of predictive modelling that is used in the development of quantum processors (qps). this method is based on real space grids and does not make assumptions related to device topology, making it widely applicable. There is a vast accessible design space; to identify the most promising options for fabrication, one requires predictive modelling of interacting electrons in real geometries and complex non ideal environments.

Ab Initio Quantum Chemistry Methods Semantic Scholar In this work we explore a modelling method based on real space grids, an ab initio approach without assumptions relating to device topology and therefore with wide applicability. Given an electrode geometry, we determine the exchange coupling between quantum dot qubits, and model the full evolution of a swap gate to predict qubit loss and infidelity rates for various. This paper presents a pimc algorithm that estimates exchange interactions of three dimensional electrically defined quantum dots, and applies this model to silicon metal oxide semiconductor (mos) devices and benchmarks the method against well tested full configuration interaction (fci) simulations. Our mission at quantum zeitgeist is to help businesses and researchers unlock the potential of quantum to solve previously.

Quantum Computing Predictive Modeler This paper presents a pimc algorithm that estimates exchange interactions of three dimensional electrically defined quantum dots, and applies this model to silicon metal oxide semiconductor (mos) devices and benchmarks the method against well tested full configuration interaction (fci) simulations. Our mission at quantum zeitgeist is to help businesses and researchers unlock the potential of quantum to solve previously. In this work we explore a modeling method based on real space grids, an ab initio approach without assumptions relating to device topology and therefore with wide applicability. The iterative nature of chip development, coupled with the time and cost of a chip design cycle, makes it highly desirable to have accurate predictive modelling tools to speed up the discovery of scalable designs. In this work we explore a modelling method based on real space grids, an ab initio approach without assumptions relating to device topology and therefore with wide applicability. Here, we develop an efficient digital twin of a two dimensional (2d) quantum processing unit (qpu) with access to a variety of compelling features, e.g. additional levels beyond the qubit states, long range interactions, and decoherence effects.