Quantitative Analysis of the Stochastic Approach to Quantum Tunneling
- URL: http://arxiv.org/abs/2009.00017v2
- Date: Fri, 25 Sep 2020 02:53:42 GMT
- Title: Quantitative Analysis of the Stochastic Approach to Quantum Tunneling
- Authors: Mark P. Hertzberg, Fabrizio Rompineve, Neil Shah
- Abstract summary: Recently there has been increasing interest in alternate methods to compute quantum tunneling in field theory.
Previous work showed parametric agreement between the tunneling rate in this approach and the usual instanton approximation.
Here we show that this approach does not in fact match precisely; the method tends to overpredict the instanton tunneling rate.
- Score: 10.675149822083915
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently there has been increasing interest in alternate methods to compute
quantum tunneling in field theory. Of particular interest is a stochastic
approach which involves (i) sampling from the free theory Gaussian
approximation to the Wigner distribution in order to obtain stochastic initial
conditions for the field and momentum conjugate, then (ii) evolving under the
classical field equations of motion, which leads to random bubble formation.
Previous work showed parametric agreement between the logarithm of the
tunneling rate in this stochastic approach and the usual instanton
approximation. However, recent work [1] claimed excellent agreement between
these methods. Here we show that this approach does not in fact match
precisely; the stochastic method tends to overpredict the instanton tunneling
rate. To quantify this, we parameterize the standard deviations in the initial
stochastic fluctuations by $\epsilon \sigma$, where $\sigma$ is the actual
standard deviation of the Gaussian distribution and $\epsilon$ is a fudge
factor; $\epsilon = 1$ is the physical value. We numerically implement the
stochastic approach to obtain the bubble formation rate for a range of
potentials in 1+1-dimensions, finding that $\epsilon$ always needs to be
somewhat smaller than unity to suppress the otherwise much larger stochastic
rates towards the instanton rates; for example, in the potential of [1] one
needs $\epsilon \approx 1/2$. We find that a mismatch in predictions also
occurs when sampling from other Wigner distributions, and in single particle
quantum mechanics even when the initial quantum system is prepared in an exact
Gaussian state. If the goal is to obtain agreement between the two methods, our
results show that the stochastic approach would be useful if a prescription to
specify optimal fudge factors for fluctuations can be developed.
Related papers
- Flow matching achieves almost minimax optimal convergence [50.38891696297888]
Flow matching (FM) has gained significant attention as a simulation-free generative model.
This paper discusses the convergence properties of FM for large sample size under the $p$-Wasserstein distance.
We establish that FM can achieve an almost minimax optimal convergence rate for $1 leq p leq 2$, presenting the first theoretical evidence that FM can reach convergence rates comparable to those of diffusion models.
arXiv Detail & Related papers (2024-05-31T14:54:51Z) - Adaptive Annealed Importance Sampling with Constant Rate Progress [68.8204255655161]
Annealed Importance Sampling (AIS) synthesizes weighted samples from an intractable distribution.
We propose the Constant Rate AIS algorithm and its efficient implementation for $alpha$-divergences.
arXiv Detail & Related papers (2023-06-27T08:15:28Z) - Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative
Models [49.81937966106691]
We develop a suite of non-asymptotic theory towards understanding the data generation process of diffusion models.
In contrast to prior works, our theory is developed based on an elementary yet versatile non-asymptotic approach.
arXiv Detail & Related papers (2023-06-15T16:30:08Z) - Stochastic optimal transport in Banach Spaces for regularized estimation
of multivariate quantiles [0.0]
We introduce a new algorithm for solving entropic optimal transport (EOT) between two absolutely continuous probability measures $mu$ and $nu$.
We study the almost sure convergence of our algorithm that takes its values in an infinite-dimensional Banach space.
arXiv Detail & Related papers (2023-02-02T10:02:01Z) - A blob method method for inhomogeneous diffusion with applications to
multi-agent control and sampling [0.6562256987706128]
We develop a deterministic particle method for the weighted porous medium equation (WPME) and prove its convergence on bounded time intervals.
Our method has natural applications to multi-agent coverage algorithms and sampling probability measures.
arXiv Detail & Related papers (2022-02-25T19:49:05Z) - Mean-Square Analysis with An Application to Optimal Dimension Dependence
of Langevin Monte Carlo [60.785586069299356]
This work provides a general framework for the non-asymotic analysis of sampling error in 2-Wasserstein distance.
Our theoretical analysis is further validated by numerical experiments.
arXiv Detail & Related papers (2021-09-08T18:00:05Z) - Random quantum circuits anti-concentrate in log depth [118.18170052022323]
We study the number of gates needed for the distribution over measurement outcomes for typical circuit instances to be anti-concentrated.
Our definition of anti-concentration is that the expected collision probability is only a constant factor larger than if the distribution were uniform.
In both the case where the gates are nearest-neighbor on a 1D ring and the case where gates are long-range, we show $O(n log(n)) gates are also sufficient.
arXiv Detail & Related papers (2020-11-24T18:44:57Z) - Almost sure convergence rates for Stochastic Gradient Descent and
Stochastic Heavy Ball [17.33867778750777]
We study gradient descent (SGD) and the heavy ball method (SHB) for the general approximation problem.
For SGD, in the convex and smooth setting, we provide the first emphalmost sure convergence emphrates for a weighted average of the iterates.
arXiv Detail & Related papers (2020-06-14T11:12:05Z) - Mean-Field Approximation to Gaussian-Softmax Integral with Application
to Uncertainty Estimation [23.38076756988258]
We propose a new single-model based approach to quantify uncertainty in deep neural networks.
We use a mean-field approximation formula to compute an analytically intractable integral.
Empirically, the proposed approach performs competitively when compared to state-of-the-art methods.
arXiv Detail & Related papers (2020-06-13T07:32:38Z) - Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and
Variance Reduction [63.41789556777387]
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP)
We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
arXiv Detail & Related papers (2020-06-04T17:51:00Z) - Stochastic gradient-free descents [8.663453034925363]
We propose gradient-free methods and accelerated gradients with momentum for solving optimization problems.
We analyze the convergence behavior of these methods under the mean-variance framework.
arXiv Detail & Related papers (2019-12-31T13:56:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.