Optimal inequalities for completely bounded polynomials and the limitations of quantum query algorithms
- URL: http://arxiv.org/abs/2609.05201v1
- Date: Fri, 04 Sep 2026 14:34:56 GMT
- Title: Optimal inequalities for completely bounded polynomials and the limitations of quantum query algorithms
- Abstract summary: We consider the problem of establishing limitations on the power of quantum query algorithms via the completely bounded method.<n>We prove several optimal functional inequalities involving different notions of completely boundeds.<n>These inequalities lead to limitings for the power of quantum query algorithms that improve on prior works.
- Score: 0.764671395172401
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the problem of establishing limitations on the power of quantum query algorithms via the completely bounded polynomial method. In particular, we prove several optimal functional inequalities involving different notions of completely bounded polynomials. These inequalities lead to limiting theorems for the power of quantum query algorithms that improve on prior works. 1. An optimal root-influence bound for block-multilinear polynomials. Prior work showed that block-multilinear polynomials $p$ of degree $t$ satisfy a root-influence bound, $\|p\|_{\text{cb}}\geq \sum_i \sqrt{\mathrm{Inf}_i[p]}/t^2$, which is stronger than the bound appearing in the Aaronson-Ambainis conjecture. We find the optimal constant in that inequality: $\|p\|_{\text{cb}}\geq \sum_i \sqrt{\mathrm{Inf}_i[p]}/t$. Since the amplitudes of quantum algorithms that query disjoint blocks of inputs-such as $t$-fold forrelation- are block-multilinear polynomials with $\|p\|_{\text{cb}}\leq 1,$ our inequality shows that they satisfy $t\geq \sum_i\sqrt{\mathrm{Inf}_i[p]}$. We prove that this inequality yields both a more efficient classical simulation than prior results based on the Aaronson-Ambainis argument, and a qualitative improvement: all classical queries are nonadaptive. 2. Optimal Fourier growth of the highest level of quantum query algorithms. We show that for every polynomial $p$ defined on $\{-1,1\}^n$ of degree $2t$, the Fourier Growth at the level $2t,$ namely $\|\widehat p_{2t}\|_{\ell_1}$, satisfies $\|\widehat p_{2t}\|_{\ell_1}\leq (en/(2t-1))^{\frac{2t-1}{2}}\|p\|_{\text{cb}}$. This is optimal up to the factor $e$, as witnessed by $2t$-fold forrelation. As quantum query algorithms that make $t$ queries (to the whole input) satisfy $\|p\|_{\text{cb}}\leq 1$, this yields a Fourier growth bound for these algorithms, partially resolving a question by Girish (STOC, 2026).
Related papers
- Quantum Algorithms and Hardness for Point-Count Approximation over Finite Fields [1.4323566945483497]
We study the approximation of the number of solutions of Laurentrms over finite fields.<n>Our first main result is a quantum algorithm that outputs $widehat(f)$ satisfying [ |widehatN(f) - N(f)| le varepsilon qn+s/2- ] with success probability $1-$.<n>As a second main result, we prove that the same problem becomes $#$P-hard under randomized-time Turing reductions.
arXiv Detail & Related papers (2026-08-25T00:23:27Z) - Faster quantum linear system solver beyond the condition number [50.84794327094274]
We present two quantum algorithms that produce the normalized solution $|xrangle$ of linear system $Ax=| b rangle$ to accuracy $$ with complexity independent of the condition number $=lVert A-1rVert$.<n>Our filtering-based solver is extremely simple with a favorable runtime prefactor.
arXiv Detail & Related papers (2026-07-08T17:49:40Z) - 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) - Quantum Algorithms and Lower Bounds for Finite-Sum Optimization [22.076317220348145]
We give a quantum algorithm with complexity $tildeObig(n+sqrtd+sqrtell/mubig)$, improving the classical tight bound $tildeThetabig(n+sqrtnell/mubig)$.
We also prove a quantum lower bound $tildeOmega(n+n3/4(ell/mu)1/4)$ when $d$ is large enough.
arXiv Detail & Related papers (2024-06-05T07:13:52Z) - A polynomial quantum computing algorithm for solving the dualization
problem [75.38606213726906]
Given two monotone prime functions $f:0,1n to 0,1$ and $g:0,1n to 0,1$ the dualization problem consists in determining if $g$ is the dual of $f$.
We present a quantum computing algorithm that solves the decision version of the dualization problem in time.
arXiv Detail & Related papers (2023-08-28T18:12:54Z) - Quantum and classical low-degree learning via a dimension-free Remez
inequality [52.12931955662553]
We show a new way to relate functions on the hypergrid to their harmonic extensions over the polytorus.
We show the supremum of a function $f$ over products of the cyclic group $exp(2pi i k/K)_k=1K$.
We extend to new spaces a recent line of work citeEI22, CHP, VZ22 that gave similarly efficient methods for learning low-degrees on hypercubes and observables on qubits.
arXiv Detail & Related papers (2023-01-04T04:15:40Z) - 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) - 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) - An Optimal Separation of Randomized and Quantum Query Complexity [67.19751155411075]
We prove that for every decision tree, the absolute values of the Fourier coefficients of a given order $ellsqrtbinomdell (1+log n)ell-1,$ sum to at most $cellsqrtbinomdell (1+log n)ell-1,$ where $n$ is the number of variables, $d$ is the tree depth, and $c>0$ is an absolute constant.
arXiv Detail & Related papers (2020-08-24T06:50:57Z) - Exact Quantum Query Algorithms Outperforming Parity -- Beyond The
Symmetric functions [3.652509571098291]
We first obtain optimal exact quantum query algorithms ($Q_algo(f)$) for a direct sum based class of $Omega left( 2fracsqrtn2 right)$ non-symmetric functions.
We show that query complexity of $Q_algo$ is $lceil frac3n4 rceil$ whereas $D_oplus(f)$ varies between $n-1$ and $lceil frac3n4 rce
arXiv Detail & Related papers (2020-08-14T12:17:48Z) - Learning sums of powers of low-degree polynomials in the non-degenerate
case [2.6109033135086777]
We give a learning algorithm for an arithmetic circuit model from a lower bound for the same model, provided certain non-degeneracy conditions hold.
Our algorithm is based on a scheme for obtaining a learning algorithm for an arithmetic circuit model from a lower bound for the same model.
arXiv Detail & Related papers (2020-04-15T06:18:41Z)
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.