Evidence for simple "arrow of time functions" in closed chaotic quantum systems
- URL: http://arxiv.org/abs/2408.08007v2
- Date: Thu, 12 Sep 2024 09:39:36 GMT
- Title: Evidence for simple "arrow of time functions" in closed chaotic quantum systems
- Authors: Merlin Füllgraf, Jiaozi Wang, Jochen Gemmer,
- Abstract summary: The construction of $alphan(t)$ from $C(t)$ requires the first $2n$ temporal derivatives of $C(t)$ at times $0$ and $t$.
Our focus is on $alphan(t)$ that (almost) monotonously decrease, we call these arrows of time functions" (AOTFs)
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Through an explicit construction, we assign to any infinite temperature autocorrelation function $C(t)$ a set of functions $\alpha^n(t)$. The construction of $\alpha^n(t)$ from $C(t)$ requires the first $2n$ temporal derivatives of $C(t)$ at times $0$ and $t$. Our focus is on $\alpha^n(t)$ that (almost) monotonously decrease, we call these ``arrows of time functions" (AOTFs). For autocorrelation functions of few body observables we numerically observe the following: An AOTF featuring a low $n$ may always be found unless the the system is in or close to a nonchaotic regime with respect to a variation of some system parameter. All $\alpha^n(t)$ put upper bounds to the respective autocorrelation functions, i.e. $\alpha^n(t) \geq C^2(t)$. Thus the implication of the existence of an AOTF is comparable to that of the H-Theorem, as it indicates a directed approach to equilibrium. We furthermore argue that our numerical finding may to some extent be traced back to the operator growth hypothesis. This argument is laid out in the framework of the so-called recursion method.
Related papers
- Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank [14.919427330415608]
We show that FISTA can improve the dependence on $$ while preserving the $1/$ locality scaling.<n>We analyze FISTA on a slightly over-regularized objective and show that, under a checkable confinement condition, all spurious activations remain inside a boundary set.<n>This yields a bound consisting of an accelerated $(sqrt)-1log(/varepsilon)$ term plus a boundary overhead $sqrtvol(mathcalB)/(3/2)$.
arXiv Detail & Related papers (2026-02-24T17:35:46Z) - What Trace Powers Reveal About Log-Determinants: Closed-Form Estimators, Certificates, and Failure Modes [0.0]
We study access to trace powers $p_k = tr(Ak)$, natural when matrix powers are available.<n>We prove a fundamental limit: no continuously positive moments can be uniformly accurate over unbounded conditioning.
arXiv Detail & Related papers (2026-01-18T23:04:17Z) - Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination [65.37519531362157]
We show that any efficient Statistical Query algorithm for this task requires VSTAT complexity at least $tildeOmega(d1/2/alpha2)$.
arXiv Detail & Related papers (2025-10-12T15:42:44Z) - A convergence law for continuous logic and continuous structures with finite domains [0.0]
We consider continuous structures with finite domain $[n] := 1, ldots, n$ and a many valued logic, $CLA$, with values in the unit interval.
$CLA$ subsumes first-order logic on conventional'' finite structures.
arXiv Detail & Related papers (2025-04-11T19:08:38Z) - Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit [75.4661041626338]
We study the problem of gradient descent learning of a single-index target function $f_*(boldsymbolx) = textstylesigma_*left(langleboldsymbolx,boldsymbolthetarangleright)$
We prove that a two-layer neural network optimized by an SGD-based algorithm learns $f_*$ with a complexity that is not governed by information exponents.
arXiv Detail & Related papers (2024-06-03T17:56:58Z) - On the $O(\frac{\sqrt{d}}{T^{1/4}})$ Convergence Rate of RMSProp and Its Momentum Extension Measured by $\ell_1$ Norm [59.65871549878937]
This paper considers the RMSProp and its momentum extension and establishes the convergence rate of $frac1Tsum_k=1T.
Our convergence rate matches the lower bound with respect to all the coefficients except the dimension $d$.
Our convergence rate can be considered to be analogous to the $frac1Tsum_k=1T.
arXiv Detail & Related papers (2024-02-01T07:21:32Z) - A Unified Framework for Uniform Signal Recovery in Nonlinear Generative
Compressed Sensing [68.80803866919123]
Under nonlinear measurements, most prior results are non-uniform, i.e., they hold with high probability for a fixed $mathbfx*$ rather than for all $mathbfx*$ simultaneously.
Our framework accommodates GCS with 1-bit/uniformly quantized observations and single index models as canonical examples.
We also develop a concentration inequality that produces tighter bounds for product processes whose index sets have low metric entropy.
arXiv Detail & Related papers (2023-09-25T17:54:19Z) - An Over-parameterized Exponential Regression [18.57735939471469]
Recent developments in the field of Large Language Models (LLMs) have sparked interest in the use of exponential activation functions.
We define the neural function $F: mathbbRd times m times mathbbRd times mathbbRd times mathbbRd times mathbbRd times mathbbRd times mathbbRd times mathbbRd
arXiv Detail & Related papers (2023-03-29T07:29:07Z) - Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD [38.221784575853796]
This work considers the problem of finding first-order stationary point of a non atau function with potentially constant smoothness using a gradient.
We develop a technique that allows us to prove $mathcalO(fracmathrmpolylog(T)sigmatT)$ convergence rates without assuming uniform bounds on the noise.
arXiv Detail & Related papers (2023-02-13T18:13:36Z) - Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent [50.659479930171585]
We study a function of the form $mathbfxmapstosigma(mathbfwcdotmathbfx)$ for monotone activations.
The goal of the learner is to output a hypothesis vector $mathbfw$ that $F(mathbbw)=C, epsilon$ with high probability.
arXiv Detail & Related papers (2022-06-17T17:55:43Z) - On the Self-Penalization Phenomenon in Feature Selection [69.16452769334367]
We describe an implicit sparsity-inducing mechanism based on over a family of kernels.
As an application, we use this sparsity-inducing mechanism to build algorithms consistent for feature selection.
arXiv Detail & Related papers (2021-10-12T09:36:41Z) - Linear Asymptotic Convergence of Anderson Acceleration: Fixed-Point
Analysis [0.0]
We study the convergence of AA($m$), i.e., Anderson acceleration with window size $m$ for accelerating fixed-point methods.
We analyze the continuity and differentiability properties of $Psi(z)$ and $beta(z)$.
arXiv Detail & Related papers (2021-09-29T03:42:41Z) - Optimal Spectral Recovery of a Planted Vector in a Subspace [80.02218763267992]
We study efficient estimation and detection of a planted vector $v$ whose $ell_4$ norm differs from that of a Gaussian vector with the same $ell$ norm.
We show that in the regime $n rho gg sqrtN$, any spectral method from a large class (and more generally, any low-degree of the input) fails to detect the planted vector.
arXiv Detail & Related papers (2021-05-31T16:10:49Z) - Simulated annealing from continuum to discretization: a convergence
analysis via the Eyring--Kramers law [10.406659081400354]
We study the convergence rate of continuous-time simulated annealing $(X_t;, t ge 0)$ and its discretization $(x_k;, k =0,1, ldots)$
We prove that the tail probability $mathbbP(f(X_t) > min f +delta)$ (resp. $mathP(f(x_k) > min f +delta)$) decays in time (resp. in cumulative step size)
arXiv Detail & Related papers (2021-02-03T23:45:39Z) - Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet
Log-Sobolev [31.57723436316983]
One approach to sample from a high dimensional distribution matrix for some function is the Langevin Algorithm.
Our work generalizes the results of [53] where $mathRn$ is defined on af$ rather than $bbRn$.
arXiv Detail & Related papers (2020-10-11T15:02:12Z) - Agnostic Learning of a Single Neuron with Gradient Descent [92.7662890047311]
We consider the problem of learning the best-fitting single neuron as measured by the expected square loss.
For the ReLU activation, our population risk guarantee is $O(mathsfOPT1/2)+epsilon$.
For the ReLU activation, our population risk guarantee is $O(mathsfOPT1/2)+epsilon$.
arXiv Detail & Related papers (2020-05-29T07:20:35Z) - Agnostic Q-learning with Function Approximation in Deterministic
Systems: Tight Bounds on Approximation Error and Sample Complexity [94.37110094442136]
We study the problem of agnostic $Q$-learning with function approximation in deterministic systems.
We show that if $delta = Oleft(rho/sqrtdim_Eright)$, then one can find the optimal policy using $Oleft(dim_Eright)$.
arXiv Detail & Related papers (2020-02-17T18:41:49Z)
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.