Lecture

Quantum Random Number Generation

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In course

PHYS-758: Advanced Course on Quantum Communication

The aim of this doctoral course by Nicolas Sangouard is to lay the theoretical groundwork that is needed for students to understand how to take advantage of quantum effects for communication technologies and contribute to on-going efforts in this field. ...

Description

This lecture covers the concept of quantum random number generation, focusing on the definition of perfectly random bits and the process of generating good randomness using quantum devices. It also discusses the implementation of quantum random number generators with qubits and the challenges faced in ensuring randomness. The lecture explores the use of quantum models and measurements to achieve randomness and the implications of device-independent quantum random number generation with a CHSH test.

Instructor

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

Ontological neighbourhood

Computer engineering

Computer security: Pseudorandomness

Theoretical computer science

Theory of computation: Quantum computing

Statistics

Statistical inference: Mathematical statistics

Related concepts (32)

Random number generation

Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model.

Quantum key distribution

Quantum key distribution (QKD) is a secure communication method that implements a cryptographic protocol involving components of quantum mechanics. It enables two parties to produce a shared random secret key known only to them, which then can be used to encrypt and decrypt messages. The process of quantum key distribution is not to be confused with quantum cryptography, as it is the best-known example of a quantum-cryptographic task.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Hardware random number generator

In computing, a hardware random number generator (HRNG), true random number generator (TRNG) or non-deterministic random bit generator (NRBG) is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a physical entropy source), rather than by means of an algorithm. Such devices are often based on microscopic phenomena that generate low-level, statistically random "noise" signals, such as thermal noise, the photoelectric effect, involving a beam splitter, and other quantum phenomena.

U.S. state

In the United States, a state is a constituent political entity, of which there are 50. Bound together in a political union, each state holds governmental jurisdiction over a separate and defined geographic territory where it shares its sovereignty with the federal government. Due to this shared sovereignty, Americans are citizens both of the federal republic and of the state in which they reside.

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