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Aaronson-Ambainis conjecture

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

Show that there exist C1,C2>0C_1,C_2>0 such that for all n≥1n\ge 1, for all f:{−1,1}n→[−1,1]f:\{-1,1\}^n\to [-1,1], we have \begin{equation} \max_{1\le j\le n}\text{Inf}_j(f)\ge C_1\left(\frac{\text{Var}(f)}{\deg(f)}\right)^{C_2}. \end{equation}

Lean 4

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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

Here we collect some problems related to the Aaronson-Ambainis Conjecture.

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

Some partial cases have been known. For example, it is true when

  • the function ff is symmetric;

  • the function ff is Boolean i.e., it takes values in {−1,1}\{-1,1\};

  • the constant deg⁡(f)\deg(f) is replaced by edeg⁡(f)e^{\deg(f)}.

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.

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