230,909 research outputs found
Experimental study of quantum random number generator based on two independent lasers
Quantum random number generator (QRNG) can produce true randomness by
utilizing the inherent probabilistic nature of quantum mechanics. Recently, the
spontaneous-emission quantum phase noise of the laser has been widely deployed
for QRNG, due to its high rate, low cost and the feasibility of chip-scale
integration. Here, we perform a comprehensive experimental study of phase-noise
based QRNG with two independent lasers, each of which operates in either
continuous-wave (CW) or pulsed mode. We implement QRNGs by operating the two
lasers in three configurations, namely CW+CW, CW+pulsed and pulsed+pulsed, and
demonstrate their tradeoffs, strengths and weaknesses.Comment: 7pages,6figures.It has been accepted by PR
Enhancing quantum entropy in vacuum-based quantum random number generator
Information-theoretically provable unique true random numbers, which cannot
be correlated or controlled by an attacker, can be generated based on quantum
measurement of vacuum state and universal-hashing randomness extraction.
Quantum entropy in the measurements decides the quality and security of the
random number generator. At the same time, it directly determine the extraction
ratio of true randomness from the raw data, in other words, it affects quantum
random numbers generating rate obviously. In this work, considering the effects
of classical noise, the best way to enhance quantum entropy in the vacuum-based
quantum random number generator is explored in the optimum dynamical
analog-digital converter (ADC) range scenario. The influence of classical noise
excursion, which may be intrinsic to a system or deliberately induced by an
eavesdropper, on the quantum entropy is derived. We propose enhancing local
oscillator intensity rather than electrical gain for noise-independent
amplification of quadrature fluctuation of vacuum state. Abundant quantum
entropy is extractable from the raw data even when classical noise excursion is
large. Experimentally, an extraction ratio of true randomness of 85.3% is
achieved by finite enhancement of the local oscillator power when classical
noise excursions of the raw data is obvious.Comment: 12 pages,8 figure
Recommendations and illustrations for the evaluation of photonic random number generators
The never-ending quest to improve the security of digital information
combined with recent improvements in hardware technology has caused the field
of random number generation to undergo a fundamental shift from relying solely
on pseudo-random algorithms to employing optical entropy sources. Despite these
significant advances on the hardware side, commonly used statistical measures
and evaluation practices remain ill-suited to understand or quantify the
optical entropy that underlies physical random number generation. We review the
state of the art in the evaluation of optical random number generation and
recommend a new paradigm: quantifying entropy generation and understanding the
physical limits of the optical sources of randomness. In order to do this, we
advocate for the separation of the physical entropy source from deterministic
post-processing in the evaluation of random number generators and for the
explicit consideration of the impact of the measurement and digitization
process on the rate of entropy production. We present the Cohen-Procaccia
estimate of the entropy rate as one way to do this. In order
to provide an illustration of our recommendations, we apply the Cohen-Procaccia
estimate as well as the entropy estimates from the new NIST draft standards for
physical random number generators to evaluate and compare three common optical
entropy sources: single photon time-of-arrival detection, chaotic lasers, and
amplified spontaneous emission
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
While super-resolution (SR) methods based on diffusion models exhibit
promising results, their practical application is hindered by the substantial
number of required inference steps. Recent methods utilize degraded images in
the initial state, thereby shortening the Markov chain. Nevertheless, these
solutions either rely on a precise formulation of the degradation process or
still necessitate a relatively lengthy generation path (e.g., 15 iterations).
To enhance inference speed, we propose a simple yet effective method for
achieving single-step SR generation, named SinSR. Specifically, we first derive
a deterministic sampling process from the most recent state-of-the-art (SOTA)
method for accelerating diffusion-based SR. This allows the mapping between the
input random noise and the generated high-resolution image to be obtained in a
reduced and acceptable number of inference steps during training. We show that
this deterministic mapping can be distilled into a student model that performs
SR within only one inference step. Additionally, we propose a novel
consistency-preserving loss to simultaneously leverage the ground-truth image
during the distillation process, ensuring that the performance of the student
model is not solely bound by the feature manifold of the teacher model,
resulting in further performance improvement. Extensive experiments conducted
on synthetic and real-world datasets demonstrate that the proposed method can
achieve comparable or even superior performance compared to both previous SOTA
methods and the teacher model, in just one sampling step, resulting in a
remarkable up to x10 speedup for inference. Our code will be released at
https://github.com/wyf0912/SinS
Deterministic networks for probabilistic computing
Neural-network models of high-level brain functions such as memory recall and
reasoning often rely on the presence of stochasticity. The majority of these
models assumes that each neuron in the functional network is equipped with its
own private source of randomness, often in the form of uncorrelated external
noise. However, both in vivo and in silico, the number of noise sources is
limited due to space and bandwidth constraints. Hence, neurons in large
networks usually need to share noise sources. Here, we show that the resulting
shared-noise correlations can significantly impair the performance of
stochastic network models. We demonstrate that this problem can be overcome by
using deterministic recurrent neural networks as sources of uncorrelated noise,
exploiting the decorrelating effect of inhibitory feedback. Consequently, even
a single recurrent network of a few hundred neurons can serve as a natural
noise source for large ensembles of functional networks, each comprising
thousands of units. We successfully apply the proposed framework to a diverse
set of binary-unit networks with different dimensionalities and entropies, as
well as to a network reproducing handwritten digits with distinct predefined
frequencies. Finally, we show that the same design transfers to functional
networks of spiking neurons.Comment: 22 pages, 11 figure
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