Simple source device-independent continuous-variable quantum random number generator

Phase-randomized optical homodyne detection is a well-known technique for performing quantum state tomography. So far, it has been mainly considered a sophisticated tool for laboratory experiments but unsuitable for practical applications. In this work, we change the perspective and employ this technique to set up a practical continuous-variable quantum random number generator. We exploit a phase-randomized local oscillator realized with a gain-switched laser to bound the min-entropy and extract true randomness from a completely uncharacterized input, potentially controlled by a malicious adversary. Our proof-of-principle implementation achieves an equivalent rate of 270 Mbit/s. In contrast to other source-device-independent quantum random number generators, the one presented herein does not require additional active optical components, thus representing a viable solution for future compact, modulator-free, certified generators of randomness

Random number generation from spontaneous Raman scattering

We investigate the generation of random numbers via the quantum process of spontaneous Raman scattering. Spontaneous Raman photons are produced by illuminating a highly nonlinear chalcogenide glass (As₂S₃) fiber with a CW laser at a power well below the stimulated Raman threshold. Single Raman photons are collected and separated into two discrete wavelength detuning bins of equal scattering probability. The sequence of photon detection clicks is converted into a random bit stream. Postprocessing is applied to remove detector bias, resulting in a final bit rate of ~650 kb/s. The collected random bit-sequences pass the NIST statistical test suite for one hundred 1 Mb samples, with the significance level set to α = 0.01. The fiber is stable, robust and the high nonlinearity (compared to silica) allows for a short fiber length and low pump power favourable for real world application.4 page(s

Robust, low-cost, auditable random number generation for embedded system security

This paper presents an architecture for a discrete, high-entropy hardware random number generator. Because it is constructed out of simple hardware components, its operation is transparent and auditable. Using avalanche noise, a nondeterministic physical phenomenon, the circuit is inherently probabilistic and resists adversarial control. Furthermore, because it compares the outputs from two matched noise sources, it rejects environmental disturbances like power supply ripple. The resulting hardware produces more than 0.98 bits of entropy per sample, is inexpensive, has a small footprint, and can be disabled to conserve power when not in use

How Many Species Are There on Earth and in the Ocean?

The diversity of life is one of the most striking aspects of our planet; hence knowing how many species inhabit Earth is among the most fundamental questions in science. Yet the answer to this question remains enigmatic, as efforts to sample the world's biodiversity to date have been limited and thus have precluded direct quantification of global species richness, and because indirect estimates rely on assumptions that have proven highly controversial. Here we show that the higher taxonomic classification of species (i.e., the assignment of species to phylum, class, order, family, and genus) follows a consistent and predictable pattern from which the total number of species in a taxonomic group can be estimated. This approach was validated against well-known taxa, and when applied to all domains of life, it predicts ∼8.7 million (±1.3 million SE) eukaryotic species globally, of which ∼2.2 million (±0.18 million SE) are marine. In spite of 250 years of taxonomic classification and over 1.2 million species already catalogued in a central database, our results suggest that some 86% of existing species on Earth and 91% of species in the ocean still await description. Renewed interest in further exploration and taxonomy is required if this significant gap in our knowledge of life on Earth is to be closed

The solution of the functional equation of D'Alembert's type for commutative groups

A functional equation of the form ϕ1(x+y)+ϕ2(x−y)=∑inαi(x)βi(y), where functions ϕ1,ϕ2,αi,βi, i=1,…,n are defined on a commutative group, is solved. We also obtain conditions for the solutions of this equation to be matrix elements of a finite dimensional representation of the group

Universal estimators of a vector parameter

Let x be a random sample with a distribution depending on a vector parameter [theta] [set membership, variant] m. The description of distributions and generalized prior densities on m is given, for which the generalized Bayes estimator of [theta], based on x, is the same for all symmetric loss functions. These distributions form a special subclass of exponential family. The specification of this result for the case of a location parameter is considered. The proof of the main theorem is based on the solution of a functional equation of D'Alembert's type.generalized Bayes estimators CS set of loss functions universal estimators exponential family functional equation of the D'Alembert's type

Risk behavior of variance estimators in multivariate normal distribution

In this paper we consider the estimation problem of unknown variance of a multivariate normal vector under quadratic loss and entropy loss. The behavior of risk functions of the Brewster--Zidek estimator and the original Stein estimator is examined. Numerical studies show that an asymptotically inadmissible Stein estimator provides a larger degree of improvement than an admissible Brewster--Zidek estimator.Variance estimation quadratic loss entropy loss Brewster--Zidek estimator Stein estimator confluent hypergeometric function