Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 147 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 28 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 58 tok/s Pro
Kimi K2 201 tok/s Pro
GPT OSS 120B 434 tok/s Pro
Claude Sonnet 4.5 38 tok/s Pro
2000 character limit reached

On a quantum-classical correspondence: from graphs to manifolds (2112.10748v2)

Published 20 Dec 2021 in math.AP and math.DG

Abstract: We establish conditions for which graph Laplacians $\Delta_{\lambda,\epsilon}$ on compact, boundaryless, smooth submanifolds $\mathcal{M}$ of Euclidean space are semiclassical pseudodifferential operators ($\Psi$DOs): essentially, that the graph Laplacian's kernel bandwidth ($\textit{bias term}$) $\sqrt{\epsilon}$ decays faster than the semiclassical parameter $h$, $\textit{i.e.}$, $h \gg \sqrt{\epsilon}$ and we compute the symbol. Coupling this with Egorov's theorem and coherent states $\psi_h$ localized at $(x_0, \xi_0) \in T*\mathcal{M}$, we show that with $U_{\lambda,\epsilon}t := e{-i t \sqrt{\Delta}{\lambda,\epsilon}}$ spectrally defined, the (co-)geodesic flow $\Gammat$ on $T*\mathcal{M}$ is approximated by $\langle U{\lambda,\epsilon}{-t} \operatorname{Op}h(a) U{\lambda,\epsilon}t \psi_h, \psi_h \rangle = a \circ \Gammat(x_0, \xi_0) + O(h)$. Then, we turn to the discrete setting: for $\Delta_{\lambda,\epsilon,N}$ a normalized graph Laplacian defined on a set of $N$ points $x_1, \ldots, x_N$ sampled $\textit{i.i.d.}$ from a probability distribution with smooth density, we establish Bernstein-type lower bounds on the probability that $||U_{\lambda,\epsilon,N}t[u] - U_{\lambda,\epsilon}t[u]||_{L{\infty}} \leq \delta$ with $U_{\lambda,\epsilon,N}t := e{-i t \sqrt{\Delta}{\lambda,\epsilon,N}}$. We apply this to coherent states to show that the geodesic flow on $\mathcal{M}$ can be approximated by matrix dynamics on the discrete sample set, namely that $\textit{with high probability}$, $c{t,N}{-1} \sum_{j=1}N |U_{\lambda,\epsilon,N}t\psi_h|2 u(x_j) = u(x_t) + O(h)$ for $c_{t,N} := \sum_{j=1}N |U_{\lambda,\epsilon,N}t\psi_h|2$ and $x_t$ the projection of $\Gammat(x_0, \xi_0)$ onto $\mathcal{M}$.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.