Lecture

Quantum Simulation with nextnano

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture introduces the nextnano software, a Schrödinger-Poisson solver for simulating complex nanostructures. It covers the input structure, material doping, and self-consistent solutions for Poisson, Drift-Diffusion, and Schrödinger Equations. The tutorial includes code examples and details on solving the Poisson equation to obtain charge density distribution and potential. It also explains the initialization, determination of potential, and visualization of output in nextnano.

Official source

Related concepts (32)

Quantum simulator

Quantum simulators permit the study of a quantum system in a programmable fashion. In this instance, simulators are special purpose devices designed to provide insight about specific physics problems. Quantum simulators may be contrasted with generally programmable "digital" quantum computers, which would be capable of solving a wider class of quantum problems. A universal quantum simulator is a quantum computer proposed by Yuri Manin in 1980 and Richard Feynman in 1982.

Charge density

In electromagnetism, charge density is the amount of electric charge per unit length, surface area, or volume. Volume charge density (symbolized by the Greek letter ρ) is the quantity of charge per unit volume, measured in the SI system in coulombs per cubic meter (C⋅m−3), at any point in a volume. Surface charge density (σ) is the quantity of charge per unit area, measured in coulombs per square meter (C⋅m−2), at any point on a surface charge distribution on a two dimensional surface.

Quantum computing

A quantum computer is a computer that exploits quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer.

Quantum programming

Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor. With quantum processor based systems, quantum programming languages help express quantum algorithms using high-level constructs.

Quantum supremacy

In quantum computing, quantum supremacy, quantum primacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates back to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.

Related lectures (64)

Introduction to nextnano: Simulation and Analysis

Covers the nextnano simulation software, focusing on input file structure, simulation process, and analysis methods.

Quantum Computing Basics

Covers the basics of quantum computing, focusing on superconducting qubits, scaling challenges, and potential applications.

Quantum Simulation: Fundamentals and Applications

Covers the fundamentals of quantum simulation, challenges in classical simulation, Trotter approximation, and applications in inelastic neutron scattering.

Quantum Physics I

Explores discrete and continuous degrees of freedom, canonical commutation relations, and the correspondence between classical and quantum mechanics.

Quantum Chemistry Simulation