OpenFrontiers
← Back to the index
OP-10170Status unassessedAnalysis

Best constants in Bohnenblust-Hille inequalities for hypercubes

Analysis on the hypercube with applications to quantum computing · AIMPL workshop record

Editorial mapping of AIMPL list topic: Analysis on the hypercube with applications to quantum computing. http://aimpl.org/hypercubequantum/ Not an author-supplied MSC code.

Workshop record · status unassessedAs of 2026-09-10 source snapshot; not a current-status certificationSource & evidence ↓

Problem statement

Fix d≥1d\ge 1. Show that there exists C>0C>0 such that for all n≥1n\ge 1 and all polynomials f=∑∣S∣≤df^(S)χSf=\sum_{|S|\le d}\widehat{f}(S)\chi_S on {−1,1}n\{-1,1\}^n of degree at most dd, we have \begin{equation*} \left(\sum_{|S|\le d}|\widehat{f}(S)|^{\frac{2d}{d+1}}\right)^{\frac{d+1}{2d}}\le d^C |f|_{\infty}. \end{equation*}

Lean 4

Not yet attached

No Lean statement or proof is attached yet. Have a formalization? Share its source to add it to this problem.

Background

Workshop introduction

We fix some notation. For all n≥1n\ge 1, any function f:{−1,1}n→Rf:\{-1,1\}^n\to \mathbb{R} has the unique Fourier expansion \begin{equation*} f=\sum_{S\subset [n]}\widehat{f}(S)\chi_S \end{equation*} where for each S⊂[n]:={1,…,n}S\subset [n]:=\{1,\dots, n\}, f^(S)∈R\widehat{f}(S)\in\mathbb{R} and χS(x):=∏j∈Sxj\chi_S(x):=\prod_{j\in S}x_j. The degree of ff is the maximal ∣S∣|S| for which f^(S)≠0\widehat{f}(S)\neq 0. For d≥1d\ge 1, ff is dd-homogeneous in case it is a linear combination of χS,∣S∣=d\chi_S,|S|=d. The variance of ff is \begin{equation*} \text{Var}(f)=\sum_{S\neq \emptyset}\widehat{f}(S)^2. \end{equation*} For 1≤j≤n1\le j\le n, the influence of jj-th variable of ff is defines as \begin{equation*} \text{Inf}j(f):=\sum{j\in S}\widehat{f}(S)^2. \end{equation*} We always equip {−1,1}n\{-1,1\}^n with the uniform probability measure and denote by ∥⋅∥p\|\cdot\|_p the corresponding LpL_p-norm, 1≤p≤∞1\le p\le \infty. For 1≤j≤n1\le j\le n, denote \begin{equation*} D_j f(x):=\frac{f(x)-f(x^{\oplus j})}{2}, \end{equation*} where f(x⊕j):=f(x1,…,xj−1,−xj,xj+1,…,xn)f(x^{\oplus j}):=f(x_1,\dots, x_{j-1}, -x_j,x_{j+1},\dots, x_n). Note that Dj2=DjD_j^2=D_j. The heat semigroup on {−1,1}n\{-1,1\}^n is Pt=e−tΔP_t=e^{-t\Delta} with Δ=∑j=1nDj\Delta=\sum_{j=1}^{n}D_j. The sensitivity of ff at x∈{−1,1}nx\in\{-1,1\}^n is defined as \begin{equation*} s(f,x):=\sharp {1\le j\le n: f(x)\neq f(x^{\oplus j})}, \end{equation*} and the sensitivity of ff is \begin{equation*} s(f):=\max{s(f,x):x\in{-1,1}^n}. \end{equation*} Similarly, the block sensitivity bs(f,x)bs(f,x) of ff at xx is the maximum number of disjoint subsets B1,…,BkB_1,\dots, B_k of [n][n] such that f(x)≠f(x⊕Bj)f(x)\neq f(x^{\oplus B_j}) for all 1≤j≤k1\le j \le k, where x⊕Bx^{\oplus B} denotes the input that flips the values in B⊂[n]B\subset [n]. The block sensitivity bs(f)bs(f) of ff is then defined as max⁡{bs(f,x):x∈{−1,1}n}\max\{bs(f,x):x\in\{-1,1\}^n\}.

Section context

See Source citation: MR3961323 and the references therein for more information.

Progress & source assessment

Dated source record, inspected 2026-09-10. Current status has not been independently reassessed. Source-reported results have not been independently verified.

Status note in the source

It is known that the inequality holds with constant Cdlog⁡dC^{\sqrt{d\log d}} for some universal C>0C>0.

References
  1. [1]Analysis on the hypercube with applications to quantum computing · Original question link
  2. [2]AIMPL source bibliography link
  3. [3]AIMPL text license · CC BY-SA 3.0 linkAIM Problem Lists (AIMPL), original contributors; CC BY-SA 3.0. Adaptation: structural TeX converted to Markdown, documented source macros expanded, and source bibliography links added. Mathematical wording retained. These adapted source texts remain under CC BY-SA 3.0.

Recorded progress

Add yours →

Nothing recorded yet. A pointer to a paper the entry is missing counts.

Machine attempts

0 recorded

Attempts run against the statement below and are recorded in full, including the failures. The point is to stop the next model — and the next person — from re-walking a road that is already known to end. Each attempt costs 25 credits.

No machine attempts recorded.

Sign in to run an attempt. New accounts start with enough credits for ten.

Discussion

0 notes

Notes are for clarifying the statement, flagging a missing reference, or recording why an approach fails. Claimed solutions belong in a submission, where they can be reviewed.

No notes yet.

Sign in to add a note.