A boolean function $f$ has Fourier dimension $k$ if its nonzero Fourier coefficients span a subspace of dimension $k$. We consider the property testing task of determining whether a function has Fourier dimension at most $k$, or is $ε$-far from being so. We show that there is a $O(k/ε)$-query quantum property tester for this problem, which we show to be almost optimal. Combined with Gopalan et al.'s classical lower bound of $Ω(2^{k/2})$, this demonstrates an exponential quantum advantage for this task. We complement this result with a $\tilde{O}(2^{k/2}/ε)$ classical tester, giving a quadratic improvement over the previous best tester, and essentially settling the classical query complexity.
A boolean function f has Fourier dimension k if its nonzero Fourier coefficients span a subspace of dimension k. We consider the property testing task of determining whether a function has Fourier dimension at most k, or is ε-far from being so. We show that there is a O(k/ε)-query quantum property tester for this problem, which we show to be almost optimal. Combined with Gopalan et al.'s classical lower bound of Ω(2k/2), this demonstrates an exponential quantum advantage for this task. We complement this result with a O~(2k/2/ε) classical tester, giving a quadratic improvement over the previous best tester, and essentially settling the classical query complexity.
Many properties of Boolean functions can be tested far more efficiently than the function itself can be learned. However, this dramatic advantage often disappears when testers are limited to random samples of f instead of adaptively chosen queries to f. In this work we investigate the quantum version of this restriction: quantum algorithms that test properties of a Boolean function f solely from copies of either the function state |f⟩∝ ∑_x|x,f(x)⟩ or the phase state |(-1)^f⟩∝ ∑_x (-1)^{f(x)}|x⟩. Quantum advantage in testing from data. For monotonicity, symmetry, and triangle-freeness, we show passive quantum testers are unboundedly or super-polynomially better than their classical passive testing counterparts. They are competitive with classic query-based testers in each case. Inadequacy of Fourier sampling. Our new testers use techniques beyond quantum Fourier sampling, and it turns out this is necessary: we show a certain class of bent functions can be tested from 𝒪(1) function states but has a sample complexity lower bound of 2^{Ω(n)} for any tester relying exclusively on Fourier and classical samples. Classical queries vs. quantum data. Our passive quantum testers are competitive with classical query-based testers, but this isn't universal: we exhibit a testing problem that can be solved from 𝒪(1) classical queries but requires Ω(2^{n/2}) function state copies. The Forrelation problem provides a separation of the same magnitude in the opposite direction, so we conclude that quantum data and classical queries are "maximally incomparable" resources for testing. Towards lower bounds. We also begin the study of lower bounds for testing from quantum data. For quantum monotonicity testing, we prove that the ensembles of [Goldreich et al., 2000; Black, 2024], which give exponential lower bounds for classical sample-based testing, do not yield any nontrivial lower bounds for testing from quantum data. New insights specific to quantum data will be required for proving copy complexity lower bounds for testing in this model.
In the k-junta testing problem, a tester has to efficiently decide whether a given function f:{0,1}n→{0,1} is a k-junta (i.e., depends on at most k of its input bits) or is ε-far from any k-junta. Our main result is a quantum algorithm for this problem with query complexity O~(k/ε) and time complexity O~(nk/ε). This quadratically improves over the query complexity of the previous best quantum junta tester, due to Atıcı and Servedio. Our tester is based on a new quantum algorithm for a gapped version of the combinatorial group testing problem, with an up to quartic improvement over the query complexity of the best classical algorithm. For our upper bound on the time complexity we give a near-linear time implementation of a shallow variant of the quantum Fourier transform over the symmetric group, similar to the Schur-Weyl transform. We also prove a lower bound of Ω(k1/3) queries for junta-testing (for constant ε).
We establish the first super-polynomial quantum advantage for the tolerant junta testing problem in the adaptive setting. Specifically, we show that within a certain parameter regime, tolerant k-junta testing with high precision can be solved using poly(k) quantum queries, whereas any classical algorithm requires at least kΩ(logk) queries.
The problem of tolerant k-junta testing is as follows: given parameters (k,ε1,ε2), with 0≤ε1<ε2≤1/2, and black-box access to a Boolean function f (defined on n variables), distinguish whether f is ε1-close to some k-junta or ε2-far from every k-junta.
We show the quantum advantage for a range of parameters close to 1/2, for example, ε1=1/2−1/k and ε2=1/2−1/(2k2). The (non-adaptive) quantum tester we use was given by a recent work of Bao, Liu, Yao, Ye, and Zhang (SOSA 2026). We slightly adapt their analysis to show that it holds in the above parameter regime. On the other hand, our classical lower bound requires substantial new ideas. Inspired by the lower bound techniques of Chen and Patel (FOCS 2023), we introduce a new hard distribution of ``yes'' instances (i.e., instances with distance at most ε1 to k-juntas) that is based on planting an ``approximate-junta'' as follows: we randomly pick k out of n coordinates, and for each fixing of the k coordinates, the 2n−k values in the restricted subcube are drawn randomly except for the set of points in an error-correcting code on which we place the same random bit. We show that this distribution is much closer to k-juntas than the uniform distribution, but on the other hand, they are indistinguishable with respect to any classical algorithm making ko(logk) queries.
The goal in the area of functions property testing is to determine whether a given black-box Boolean function has a particular given property or is ε-far from having that property. We investigate here several types of properties testing for Boolean functions (identity, correlations and balancedness) using the Deutsch-Jozsa algorithm (for the Deutsch-Jozsa (D-J) problem) and also the amplitude amplification technique. At first, we study here a particular testing problem: namely whether a given Boolean function f, of n variables, is identical with a given function g or is ε-far from g, where ε is the parameter. We present a one-sided error quantum algorithm to deal with this problem that has the query complexity O(ε1). Moreover, we show that our quantum algorithm is optimal. Afterwards we show that the classical randomized query complexity of this problem is Θ(ε1). Secondly, we consider the D-J problem from the perspective of functional correlations and let C(f,g) denote the correlation of f and g. We propose an exact quantum algorithm for making distinction between ∣C(f,g)∣=ε and ∣C(f,g)∣=1 using six queries, while the classical deterministic query complexity for this problem is Θ(2n) queries. Finally, we propose a one-sided error quantum query algorithm for testing whether one Boolean function is balanced versus ε-far balanced using O(ε1) queries. We also prove here that our quantum algorithm for balancedness testing is optimal. At the same time, for this balancedness testing problem we present a classical randomized algorithm with query complexity of O(1/ε2). Also this randomized algorithm is optimal. Besides, we link the problems considered here together and generalize them to the general case. Comment: 24 pages, comments are welcome. It is accepted for publishing in Fundamenta Informaticae
Junta testing for Boolean functions has sparked a long line of work over recent decades in theoretical computer science, and recently has also been studied for unitary operators in quantum computing. Tolerant junta testing is more general and challenging than the standard version. While optimal tolerant junta testers have been obtained for Boolean functions, there has been no knowledge about tolerant junta testers for unitary operators, which was thus left as an open problem in [Chen, Nadimpalli, and Yuen, SODA2023]. In this paper, we settle this problem by presenting the first algorithm to decide whether a unitary is ε1-close to some quantum k-junta or is ε2-far from any quantum k-junta, where an n-qubit unitary U is called a quantum k-junta if it only non-trivially acts on just k of the n qubits. More specifically, we present a tolerant tester with ε1=8ρε, ε2=ε, and ρ∈(0,1), and the query complexity is O(ε2ρ(1−ρ)kklogk), which demonstrates a trade-off between the amount of tolerance and the query complexity. Note that our algorithm is non-adaptive which is preferred over its adaptive counterparts, due to its simpler as well as highly parallelizable nature. At the same time, our algorithm does not need access to U†, whereas this is usually required in the literature.
The goal in the area of functions property testing is to determine whether a given black-box Boolean function has a particular given property or is ε-far from having that property. We investigate here several types of properties testing for Boolean functions (identity, correlations and balancedness) using the Deutsch-Jozsa algorithm (for the Deutsch-Jozsa (D-J) problem) and also the amplitude amplification technique. At first, we study here a particular testing problem: namely whether a given Boolean function f, of n variables, is identical with a given function g or is ε-far from g, where ε is the parameter. We present a one-sided error quantum algorithm to deal with this problem that has the query complexity O(ε1). Moreover, we show that our quantum algorithm is optimal. Afterwards we show that the classical randomized query complexity of this problem is Θ(ε1). Secondly, we consider the D-J problem from the perspective of functional correlations and let C(f,g) denote the correlation of f and g. We propose an exact quantum algorithm for making distinction between ∣C(f,g)∣=ε and ∣C(f,g)∣=1 using six queries, while the classical deterministic query complexity for this problem is Θ(2n) queries. Finally, we propose a one-sided error quantum query algorithm for testing whether one Boolean function is balanced versus ε-far balanced using O(ε1) queries. We also prove here that our quantum algorithm for balancedness testing is optimal. At the same time, for this balancedness testing problem we present a classical randomized algorithm with query complexity of O(1/ε2). Also this randomized algorithm is optimal. Besides, we link the problems considered here together and generalize them to the general case. Comment: 24 pages, comments are welcome. It is accepted for publishing in Fundamenta Informaticae