Exploring phase space with Neural Importance Sampling
نویسندگان
چکیده
منابع مشابه
Sampling quantum phase space with squeezed states.
We study the application of squeezed states in a quantum optical scheme for direct sampling of the phase space by photon counting. We prove that the detection setup with a squeezed coherent probe field is equivalent to the probing of the squeezed signal field with a coherent state. An example of the Schr odinger cat state measurement shows that the use of squeezed states allows one to detect cl...
متن کاملAnnealed Importance Sampling for Neural Mass Models
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorit...
متن کاملImportance sampling the Rayleigh phase function.
Rayleigh scattering is used frequently in Monte Carlo simulation of multiple scattering. The Rayleigh phase function is quite simple, and one might expect that it should be simple to importance sample it efficiently. However, there seems to be no one good way of sampling it in the literature. This paper provides the details of several different techniques for importance sampling the Rayleigh ph...
متن کاملPhoton Counting Sampling of Phase Space
First complete experimental characterisation of the quantum state of a single light mode was demonstrated by Smithey et al. In their work, numerical tomographic algorithms were applied to reconstruct the Wigner function from homodyne statistics. Recently, a novel scheme for measuring the quantum state of light by photon counting has been proposed. In contrast to tomographic techniques, the newl...
متن کاملStochastic Optimization with Importance Sampling
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a ra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SciPost Physics
سال: 2020
ISSN: 2542-4653
DOI: 10.21468/scipostphys.8.4.069