Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Stochastic Numerical Methods for Quantum Systems

In course

PHYS-744: Advanced Topics in Quantum Sciences and Technologies

This course provides an in-depth treatment of the latest experimental and theoretical topics in quantum sciences and technologies, including for example quantum sensing, quantum optics, cold atoms, theory of quantum measurements and open dissipative quantu ...

Description

This lecture covers topics such as exact diagonalization, iterative diagonalization, real-time dynamics, quantum information, mean field states, matrix product state form, Metropolis-Hastings algorithm, and computing properties as stochastic averages. The instructor discusses the practical implementation in Python and provides examples related to many-body quantum systems.

Instructors (5)

Vincenzo Savona

Vincenzo Savona studied physics in Pisa at the Scuola Normale Superiore and the University of Pisa, prior to completing his PhD at the EPFL's Institute of Theoretical Physics. Subsequently he did post-doctoral work, first at the EPFL and then in the physics department of the Humboldt University of Berlin. In 2002, he returned to the EPFL to create his own research group, receiving a "professeur boursier" fellowship from the Swiss National Science Foundation. In 2006, he was appointed tenure-track assistant professor at the EPFL and joined the NCCR for Quantum Photonics. In 2010 he was appointed associate professor. Currently he directs the Laboratory of Theoretical Physics of Nanosystems.

Mayeul Sylvain Chipaux

Jean-Philippe Brantut

I did my PhD at the Institut d'Optique under the direction of Philippe Bouyer and Alain Aspect, before moving to ETHZ in the group of Tilman Esslinger, first as a post-doc then as a senior scientist. There I developed in particular the method allowing for a quantum simulation of nano-electronic devices with ultracold quantum gases.Since september 2016, I hold the Fondation Sandoz chair in physics of quantum gases at EPFL. There, my group has developed the first cold atoms machine combining Fermi gases with cavity-quantum electrodynamics. We use it to explore new ways of measuring and manipulating quantum matter.

Zoë Holmes

Christophe Marcel Georges Galland

I studied at Ecole Polytechnique in Paris (X2003) and received my PhD in 2010 from ETH Zürich for a thesis in solid-state quantum optics with individual carbon nanotubes, in the Quantum Photonics Group of Prof. Ataç Imamoglu. As a postdoctoral researcher at Los Alamos National Lab (USA) I studied the photophysics of individual nanocrystal quantum dots in the groups of Victor Klimov and Han Htoon. I was investigating the mechanisms responsible for fluorescence fluctuations and how to control them. I then moved to the University of Delaware in the group of Michael Hochberg to work in the emerging field of integrated quantum optics. I was leading international projects such as the realisation of an on-chip source of quantum correlated photons integrating optical filters and demultiplexers. From 2013 to 2016, I was working at EPFL in the group of Prof. Kippenberg in the field of quantum optomechanics with an Ambizione Fellowship of the Swiss National Science Foundation (SNSF). My work focused on the creation of non-classical vibrational states of mesoscopic oscillators and on the amplification of vibrations in molecules. Since May 2017, I am leading the Laboratory of Quantum and Nano-Optics at EPFL as an SNSF-funded professor in the Institute of Physics. My team investigates two main phenomena: (i) the vibrational dynamics of molecules embedded in nanoscale plasmonic cavities, and (ii) non-classical correlations mediated by individual quanta of crystal vibrations at room-temperature. We employ state-of-the-art spectroscopic tools such as femtosecond lasers and single-photon counters to get new insights into sub-nanometer scale dynamics.

Official source

Related concepts (32)

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 information

Quantum information is the information of the state of a quantum system. It is the basic entity of study in quantum information theory, and can be manipulated using quantum information processing techniques. Quantum information refers to both the technical definition in terms of Von Neumann entropy and the general computational term. It is an interdisciplinary field that involves quantum mechanics, computer science, information theory, philosophy and cryptography among other fields.

Matrix (mathematics)

In mathematics, a matrix (plural matrices) is a rectangular array or table of numbers, symbols, or expressions, arranged in rows and columns, which is used to represent a mathematical object or a property of such an object. For example, is a matrix with two rows and three columns. This is often referred to as a "two by three matrix", a " matrix", or a matrix of dimension . Without further specifications, matrices represent linear maps, and allow explicit computations in linear algebra.

Diagonal matrix

In linear algebra, a diagonal matrix is a matrix in which the entries outside the main diagonal are all zero; the term usually refers to square matrices. Elements of the main diagonal can either be zero or nonzero. An example of a 2×2 diagonal matrix is , while an example of a 3×3 diagonal matrix is. An identity matrix of any size, or any multiple of it (a scalar matrix), is a diagonal matrix. A diagonal matrix is sometimes called a scaling matrix, since matrix multiplication with it results in changing scale (size).

Invertible matrix

In linear algebra, an n-by-n square matrix A is called invertible (also nonsingular, nondegenerate or (rarely used) regular), if there exists an n-by-n square matrix B such that where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. If this is the case, then the matrix B is uniquely determined by A, and is called the (multiplicative) inverse of A, denoted by A−1. Matrix inversion is the process of finding the matrix B that satisfies the prior equation for a given invertible matrix A.

Related lectures (54)

Quantum Information: Density Matrices

Explores density matrices, quantum states representation, and entropy in quantum information.

Quantum Information Principles

Covers the mathematical principles of quantum physics, including Hilbert spaces and quantum bits.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

Matrix to Density Operator

Explains the transformation from a matrix to the density operator in quantum physics.

Hamiltonian of Heisenberg: Interaction and Energy Levels