Forrelation is Extremally Hard
- URL: http://arxiv.org/abs/2508.02514v1
- Date: Mon, 04 Aug 2025 15:19:19 GMT
- Title: Forrelation is Extremally Hard
- Authors: Uma Girish, Rocco Servedio,
- Abstract summary: The Forrelation problem is a central problem that demonstrates an exponential separation between quantum and classical capabilities.<n>We show that this problem can be solved with one quantum query and success probability one, yet requires $tildeOmegaleft (2n/4right)$ classical randomized queries.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Forrelation problem is a central problem that demonstrates an exponential separation between quantum and classical capabilities. In this problem, given query access to $n$-bit Boolean functions $f$ and $g$, the goal is to estimate the Forrelation function $\mathrm{forr}(f,g)$, which measures the correlation between $g$ and the Fourier transform of $f$. In this work we provide a new linear algebraic perspective on the Forrelation problem, as opposed to prior analytic approaches. We establish a connection between the Forrelation problem and bent Boolean functions and through this connection, analyze an extremal version of the Forrelation problem where the goal is to distinguish between extremal instances of Forrelation, namely $(f,g)$ with $\mathrm{forr}(f,g)=1$ and $\mathrm{forr}(f,g)=-1$. We show that this problem can be solved with one quantum query and success probability one, yet requires $\tilde{\Omega}\left(2^{n/4}\right)$ classical randomized queries, even for algorithms with a one-third failure probability, highlighting the remarkable power of one exact quantum query. We also study a restricted variant of this problem where the inputs $f,g$ are computable by small classical circuits and show classical hardness under cryptographic assumptions.
Related papers
- Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent [83.85536329832722]
We solve the $k$-sparse parity problem with sign gradient descent (SGD) on two-layer fully-connected neural networks.<n>We show that this approach can efficiently solve the $k$-sparse parity problem on a $d$-dimensional hypercube.<n>We then demonstrate how a trained neural network with sign SGD can effectively approximate this good network, solving the $k$-parity problem with small statistical errors.
arXiv Detail & Related papers (2024-04-18T17:57:53Z) - Quantum advantage in zero-error function computation with side information [10.0060346233449]
We consider the problem of zero-error function computation with side information.<n>Alice and Bob have correlated sources $X,Y$ with joint p.m.f. $p_XY(cdot, cdot)$. Bob wants to calculate $f(X,Y)$ with zero error.
arXiv Detail & Related papers (2024-02-02T16:41:36Z) - Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature
and Bayesian Optimization [51.533164528799084]
We show that to estimate the normalizing constant within a small relative error, the level of difficulty depends on the value of $lambda$.
We find that this pattern holds true even when the function evaluations are noisy.
arXiv Detail & Related papers (2024-01-11T07:45:09Z) - Agnostically Learning Multi-index Models with Queries [54.290489524576756]
We study the power of query access for the task of agnostic learning under the Gaussian distribution.
We show that query access gives significant runtime improvements over random examples for agnostically learning MIMs.
arXiv Detail & Related papers (2023-12-27T15:50:47Z) - A Law of Robustness beyond Isoperimetry [84.33752026418045]
We prove a Lipschitzness lower bound $Omega(sqrtn/p)$ of robustness of interpolating neural network parameters on arbitrary distributions.
We then show the potential benefit of overparametrization for smooth data when $n=mathrmpoly(d)$.
We disprove the potential existence of an $O(1)$-Lipschitz robust interpolating function when $n=exp(omega(d))$.
arXiv Detail & Related papers (2022-02-23T16:10:23Z) - Feature Cross Search via Submodular Optimization [58.15569071608769]
We study feature cross search as a fundamental primitive in feature engineering.
We show that there exists a simple greedy $(1-1/e)$-approximation algorithm for this problem.
arXiv Detail & Related papers (2021-07-05T16:58:31Z) - Following Forrelation -- Quantum Algorithms in Exploring Boolean
Functions' Spectra [3.2498534294827044]
We revisit the quantum algorithms for obtaining Forrelation values.
We introduce the existing 2-fold Forrelation formulation with bent duality based promise problems.
We tweak the quantum algorithm with superposition of linear functions to obtain a cross-correlation sampling technique.
arXiv Detail & Related papers (2021-04-25T17:13:18Z) - Classical algorithms for Forrelation [2.624902795082451]
We study the forrelation problem: given a pair of $n$-bit Boolean functions $f$ and $g$, estimate the correlation between $f$ and the Fourier transform of $g$.
This problem is known to provide the largest possible quantum speedup in terms of its query complexity.
We show that the graph-based forrelation problem can be solved on a classical computer in time $O(n)$ for any bipartite graph.
arXiv Detail & Related papers (2021-02-13T17:25:41Z) - $k$-Forrelation Optimally Separates Quantum and Classical Query
Complexity [3.4984289152418753]
We show that any partial function on $N$ bits can be computed with an advantage $delta$ over a random guess by making $q$ quantum queries.
We also conjectured the $k$-Forrelation problem -- a partial function that can be computed with $q = lceil k/2 rceil$ quantum queries.
arXiv Detail & Related papers (2020-08-16T21:26:46Z) - Streaming Complexity of SVMs [110.63976030971106]
We study the space complexity of solving the bias-regularized SVM problem in the streaming model.
We show that for both problems, for dimensions of $frac1lambdaepsilon$, one can obtain streaming algorithms with spacely smaller than $frac1lambdaepsilon$.
arXiv Detail & Related papers (2020-07-07T17:10:00Z) - Lower Bounds for XOR of Forrelations [7.510385608531827]
We study the XOR of $k$ independent copies of the Forrelation function.
We also show that any constant-depth circuit of quasipolynomial size has quasipolynomially small advantage over a random guess.
arXiv Detail & Related papers (2020-07-07T17:05:09Z) - Towards Optimal Separations between Quantum and Randomized Query
Complexities [0.30458514384586394]
We show that a quantum algorithm can be solved by making $2O(k)$ queries to the inputs.
For any constant $varepsilon>0$, this gives a $O(1)$ vs. $N2/3-varepsilon$ separation.
arXiv Detail & Related papers (2019-12-29T01:42:31Z)
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.