Area law for the maximally mixed ground state in degenerate 1D gapped
systems
- URL: http://arxiv.org/abs/2310.19028v1
- Date: Sun, 29 Oct 2023 14:36:30 GMT
- Title: Area law for the maximally mixed ground state in degenerate 1D gapped
systems
- Authors: Itai Arad, Raz Firanko, Rahul Jain
- Abstract summary: We show an area law with logarithmic correction for the maximally mixed state $Omega$ in the (degenerate) ground space of a 1D gapped local Hamiltonian $H$.
We also get an area law for the mutual information of the form $mathrmI(L:R)_Omega leq O(log |L|)$.
- Score: 5.088702935364181
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We show an area law with logarithmic correction for the maximally mixed state
$\Omega$ in the (degenerate) ground space of a 1D gapped local Hamiltonian $H$,
which is independent of the underlying ground space degeneracy. Formally, for
$\varepsilon>0$ and a bi-partition $L\cup L^c$ of the 1D lattice, we show that
$$\mathrm{I}^{\varepsilon}_{\max}(L:L^c)_{\Omega} \leq
O(\log(|L|)+\log(1/\varepsilon)),$$
where $|L|$ represents the number of qudits in $L$ and
$\mathrm{I}^{\epsilon}_{\max}(L:L^c)_{\Omega}$ represents the $\varepsilon$-
'smoothed maximum mutual information' with respect to the $L:L^c$ partition in
$\Omega$. As a corollary, we get an area law for the mutual information of the
form $\mathrm{I}(L:R)_\Omega \leq O(\log |L|)$. In addition, we show that
$\Omega$ can be approximated up to an $\varepsilon$ in trace norm with a state
of Schmidt rank of at most $\mathrm{poly}(|L|/\varepsilon)$.
Related papers
- Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs [54.28273395444243]
We show that the Monotonic Value Omega (MVP) algorithm achieves a variance-aware gap-dependent regret bound of $$tildeOleft(left(sum_Delta_h(s,a)>0 fracH2 log K land mathttVar_maxtextc$.
arXiv Detail & Related papers (2025-06-06T20:33:57Z) - Nonparametric MLE for Gaussian Location Mixtures: Certified Computation and Generic Behavior [28.71736321665378]
We study the nonparametric maximum likelihood estimator $widehatpi$ for Gaussian location mixtures in one dimension.<n>We provide an algorithm which for small enough $varepsilon>0$ computes an $varepsilon$-approximation of $widehatpi$ in Wasserstein distance in time.<n>We also show the distribution of $widehatpi$ conditioned to be $k$-atomic admits a density on the associated $2k-1$ dimensional parameter space.
arXiv Detail & Related papers (2025-03-26T03:36:36Z) - Sparsifying Suprema of Gaussian Processes [6.638504164134713]
We show that there is an $O_varepsilon(1)$-size subset $S subseteq T$ and a set of real values $c_s_s in S$.
We also use our sparsification result for suprema of centered Gaussian processes to give a sparsification lemma for convex sets of bounded geometric width.
arXiv Detail & Related papers (2024-11-22T01:43:58Z) - The Communication Complexity of Approximating Matrix Rank [50.6867896228563]
We show that this problem has randomized communication complexity $Omega(frac1kcdot n2log|mathbbF|)$.
As an application, we obtain an $Omega(frac1kcdot n2log|mathbbF|)$ space lower bound for any streaming algorithm with $k$ passes.
arXiv Detail & Related papers (2024-10-26T06:21:42Z) - $\ell_p$-Regression in the Arbitrary Partition Model of Communication [59.89387020011663]
We consider the randomized communication complexity of the distributed $ell_p$-regression problem in the coordinator model.
For $p = 2$, i.e., least squares regression, we give the first optimal bound of $tildeTheta(sd2 + sd/epsilon)$ bits.
For $p in (1,2)$,we obtain an $tildeO(sd2/epsilon + sd/mathrmpoly(epsilon)$ upper bound.
arXiv Detail & Related papers (2023-07-11T08:51:53Z) - $\boldsymbol{\alpha_{>}(\epsilon) = \alpha_{<}(\epsilon)}$ For The
Margolus-Levitin Quantum Speed Limit Bound [0.0]
I show that $alpha_>(epsilon)$ is indeed equal to $alpha_(epsilon)$.
I also point out a numerical stability issue in computing $alpha_>(epsilon)$.
arXiv Detail & Related papers (2023-05-17T10:07:31Z) - Krylov Methods are (nearly) Optimal for Low-Rank Approximation [8.017116107657206]
We show that any algorithm requires $Omegaleft(log(n)/varepsilon1/2right)$ matrix-vector products, exactly matching the upper bound obtained by Krylov methods.
Our lower bound addresses Open Question 1WooWoo14, providing evidence for the lack of progress on algorithms for Spectral LRA.
arXiv Detail & Related papers (2023-04-06T16:15:19Z) - Sparse Dimensionality Reduction Revisited [13.170012290527017]
The sparse Johnson-Lindenstrauss transform is one of the central techniques in dimensionality reduction.
We revisit sparse embeddings and identify a loophole in the lower bound.
We also improve the sparsity of the best oblivious subspace embeddings for optimal embedding dimensionality.
arXiv Detail & Related papers (2023-02-13T08:01:25Z) - The Approximate Degree of DNF and CNF Formulas [95.94432031144716]
For every $delta>0,$ we construct CNF and formulas of size with approximate degree $Omega(n1-delta),$ essentially matching the trivial upper bound of $n.
We show that for every $delta>0$, these models require $Omega(n1-delta)$, $Omega(n/4kk2)1-delta$, and $Omega(n/4kk2)1-delta$, respectively.
arXiv Detail & Related papers (2022-09-04T10:01:39Z) - 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) - Low-degree learning and the metric entropy of polynomials [44.99833362998488]
We prove that any (deterministic or randomized) algorithm which learns $mathscrF_nd$ with $L$-accuracy $varepsilon$ requires at least $Omega(sqrtvarepsilon)2dlog n leq log mathsfM(mathscrF_n,d,|cdot|_L,varepsilon) satisfies the two-sided estimate $$c (1-varepsilon)2dlog
arXiv Detail & Related papers (2022-03-17T23:52:08Z) - Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products [58.05771390012827]
We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm.
Our main result is an algorithm that uses only $tildeO(k/sqrtepsilon)$ matrix-vector products.
arXiv Detail & Related papers (2022-02-10T16:10:41Z) - Learning low-degree functions from a logarithmic number of random
queries [77.34726150561087]
We prove that for any integer $ninmathbbN$, $din1,ldots,n$ and any $varepsilon,deltain(0,1)$, a bounded function $f:-1,1nto[-1,1]$ of degree at most $d$ can be learned.
arXiv Detail & Related papers (2021-09-21T13:19:04Z) - Threshold Phenomena in Learning Halfspaces with Massart Noise [56.01192577666607]
We study the problem of PAC learning halfspaces on $mathbbRd$ with Massart noise under Gaussian marginals.
Our results qualitatively characterize the complexity of learning halfspaces in the Massart model.
arXiv Detail & Related papers (2021-08-19T16:16:48Z) - Approximate Maximum Halfspace Discrepancy [6.35821487778241]
We consider the range space $(X, mathcalH_d)$ where $X subset mathbbRd$ and $mathcalH_d$ is the set of ranges defined by $d$ halfspaces.
For each halfspace $h in mathcalH_d$ define a function $Phi(h)$ that measures the "difference" between the fraction of red and fraction of blue points which fall in the range $h$.
arXiv Detail & Related papers (2021-06-25T19:14:45Z) - Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss [76.02734481158458]
It is known that in the worst case, to obtain a good rank-$k$ approximation to a matrix, one needs an arbitrarily large $nOmega(1)$ number of columns.
We show that under certain minimal and realistic distributional settings, it is possible to obtain a $(k/epsilon)$-approximation with a nearly linear running time and poly$(k/epsilon)+O(klog n)$ columns.
This is the first algorithm of any kind for achieving a $(k/epsilon)$-approximation for entrywise
arXiv Detail & Related papers (2020-04-16T22:57:06Z)
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.