Quantum noise models for semiconductor lasers: is there a missing noise source?

1997 ◽  
Vol 44 (10) ◽  
pp. 1929-1936 ◽  
Author(s):  
A. BRAMATI, V. JOST, F. MARIN and E. G
1997 ◽  
Vol 44 (10) ◽  
pp. 1929-1935 ◽  
Author(s):  
A. Bramati ◽  
V. Jost ◽  
F. Marin ◽  
E. Giacobino

1972 ◽  
Vol 11 (1) ◽  
pp. 243-253 ◽  
Author(s):  
D. J. Morgan ◽  
M. J. Adams

2003 ◽  
Vol 17 (22n24) ◽  
pp. 4123-4138 ◽  
Author(s):  
Wing-Shun Lam ◽  
Parvez N. Guzdar ◽  
Rajarshi Roy

The dynamical behavior of power dropouts in a semiconductor laser with optical feedback, pumped near threshold current, is strongly influenced by quantum noise. This is clearly demonstrated by experiments with modulations on the pumping current or the feedback strength. For the cases without modulation and with only current modulation, the dropouts occur randomly. However the feedback strength modulation locks the dropout events periodically. By numerically modeling these three cases using the Lang–Kobayashi equations with a stochastic term to take into account spontaneous emission noise, it is shown that the observed behavior of the dropouts can be readily reproduced for all three cases. Noise plays a signifcant role in explaining the observed dropout events. A simple explanation of the observed dropout phenomenon is presented, based on the adiabatic motion of the ellipse formed by the steady state solutions of the rate equations due to slow time modulations of the injection current or the feedback strength.


Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 549
Author(s):  
Steven T. Flammia ◽  
Ryan O'Donnell

Motivated by estimation of quantum noise models, we study the problem of learning a Pauli channel, or more generally the Pauli error rates of an arbitrary channel. By employing a novel reduction to the "Population Recovery" problem, we give an extremely simple algorithm that learns the Pauli error rates of an n-qubit channel to precision ϵ in ℓ∞ using just O(1/ϵ2)log⁡(n/ϵ) applications of the channel. This is optimal up to the logarithmic factors. Our algorithm uses only unentangled state preparation and measurements, and the post-measurement classical runtime is just an O(1/ϵ) factor larger than the measurement data size. It is also impervious to a limited model of measurement noise where heralded measurement failures occur independently with probability ≤1/4.We then consider the case where the noise channel is close to the identity, meaning that the no-error outcome occurs with probability 1−η. In the regime of small η we extend our algorithm to achieve multiplicative precision 1±ϵ (i.e., additive precision ϵη) using just O(1ϵ2η)log⁡(n/ϵ) applications of the channel.


2016 ◽  
Vol 41 (2) ◽  
pp. 309-314
Author(s):  
Diana Ioana Popescu ◽  
Ioan Cosma

Abstract The paper presents two theoretical models for traffic noise level distribution on curved horizontal roads. In the case of vehicles moving on a given route, one can consider, in terms of sound field, that the granular traffic is equivalent for short periods with a quasi-continuous noise flow. When computing and modelling the noise level generated by traffic on roads with complex trajectory, it is common to treat the route as a sum of small length road segments, each being assimilated with a linear noise source. This paper started from the assumption that the route can be decomposed into a sequence of linear and arc-shaped road segments, each of which is treated as a linear respectively curved noise source. An arc-shaped road segment is modelled by a tubular vibrating surface, of circular or rectangular section. In the case of rectangular section, the vibrating blade emits complex sounds on its both vertical sides and the generated sound field can be described more clearly, qualitatively and quantitatively, through intensity distribution. The theoretical models presented in the paper have direct application to the traffic noise prediction and noise maps drawing


Sign in / Sign up

Export Citation Format

Share Document