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We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves…

We consider the problem of testing whether an unknown Boolean function $f$ is monotone versus $\epsilon$-far from every monotone function. The two main results of this paper are a new lower bound and a new algorithm for this well-studied…

Computational Complexity · Computer Science 2014-12-19 Xi Chen , Rocco A. Servedio , Li-Yang Tan

Approximate Counting refers to the problem where we are given query access to a function $f : [N] \to \{0,1\}$, and we wish to estimate $K = #\{x : f(x) = 1\}$ to within a factor of $1+\epsilon$ (with high probability), while minimizing the…

Quantum Physics · Physics 2020-10-12 Ramgopal Venkateswaran , Ryan O'Donnell

Randomness is a fundamental resource in quantum information, with crucial applications in cryptography, algorithms, and error correction. A central challenge is to construct unitary $k$-designs that closely approximate Haar-random unitaries…

Quantum Physics · Physics 2025-10-10 Lennart Bittel , Lorenzo Leone

We construct $\varepsilon$-approximate unitary $k$-designs on $n$ qubits in circuit depth $O(\log k \log \log n k / \varepsilon)$. The depth is exponentially improved over all known results in all three parameters $n$, $k$, $\varepsilon$.…

Quantum Physics · Physics 2025-07-22 Laura Cui , Thomas Schuster , Fernando Brandao , Hsin-Yuan Huang

In this work, we consider the problems of learning junta distributions, their quantum counterparts (quantum junta states) and $\mathsf{QAC}^0$ circuits, which we show to be close to juntas. (1) Junta distributions. A probability…

Quantum Physics · Physics 2026-05-21 Jinge Bao , Francisco Escudero-Gutiérrez

We give the first agnostic, efficient, proper learning algorithm for monotone Boolean functions. Given $2^{\tilde{O}(\sqrt{n}/\varepsilon)}$ uniformly random examples of an unknown function $f:\{\pm 1\}^n \rightarrow \{\pm 1\}$, our…

Data Structures and Algorithms · Computer Science 2023-05-25 Jane Lange , Arsen Vasilyan

Let a Boolean function be available as a black-box (oracle) and one likes to devise an algorithm to test whether it has certain property or it is $\epsilon$-far from having that property. The efficiency of the algorithm is judged by the…

Quantum Physics · Physics 2013-06-27 Kaushik Chakraborty , Subhamoy Maitra

The Quantitative Group Testing (QGT) is about learning a (hidden) subset $K$ of some large domain $N$ using a sequence of queries, where a result of a query provides information about the size of the intersection of the query with the…

Data Structures and Algorithms · Computer Science 2022-04-22 Dariusz R. Kowalski , Dominik Pajak

We consider the classic question of state tomography: given copies of an unknown quantum state $\rho\in\mathbb{C}^{d\times d}$, output $\widehat{\rho}$ which is close to $\rho$ in some sense, e.g. trace distance or fidelity. When one is…

Quantum Physics · Physics 2023-05-31 Sitan Chen , Brice Huang , Jerry Li , Allen Liu , Mark Sellke

We present an algorithm for efficiently approximating of qubit unitaries over gate sets derived from totally definite quaternion algebras. It achieves $\varepsilon$-approximations using circuits of length $O(\log(1/\varepsilon))$, which is…

Quantum Physics · Physics 2015-10-16 Vadym Kliuchnikov , Alex Bocharov , Martin Roetteler , Jon Yard

We present a variational algorithm for fault tolerant quantum computing to solve a system of linear equations which directly maximises the parameters of the target fidelity. This so-called measurement test algorithm can be applied to any…

Quantum Physics · Physics 2026-04-30 Alain Giresse Tene , Thomas Konrad

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions. Closeness testing is the problem of distinguishing whether two $n$-dimensional…

Quantum Physics · Physics 2024-06-27 Jingquan Luo , Qisheng Wang , Lvzhou Li

We study the problem of testing unateness of functions $f:\{0,1\}^d \to \mathbb{R}.$ We give a $O(\frac{d}{\epsilon} \cdot \log\frac{d}{\epsilon})$-query nonadaptive tester and a $O(\frac{d}{\epsilon})$-query adaptive tester and show that…

Data Structures and Algorithms · Computer Science 2017-03-16 Roksana Baleshzar , Deeparnab Chakrabarty , Ramesh Krishnan S. Pallavoor , Sofya Raskhodnikova , C. Seshadhri

We show that every algorithm for testing $n$-variate Boolean functions for monotonicity must have query complexity $\tilde{\Omega}(n^{1/4})$. All previous lower bounds for this problem were designed for non-adaptive algorithms and, as a…

Computational Complexity · Computer Science 2015-11-17 Aleksandrs Belovs , Eric Blais

The limited computational power of constant-depth quantum circuits can be boosted by adapting future gates according to the outcomes of mid-circuit measurements. We formulate computation of a variety of Boolean functions in the framework of…

Quantum Physics · Physics 2024-12-02 Austin K. Daniel , Akimasa Miyake

We extend three related results from the analysis of influences of Boolean functions to the quantum setting, namely the KKL Theorem, Friedgut's Junta Theorem and Talagrand's variance inequality for geometric influences. Our results are…

Functional Analysis · Mathematics 2024-04-05 Cambyse Rouzé , Melchior Wirth , Haonan Zhang

Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns yes if the points are in…

Data Structures and Algorithms · Computer Science 2025-04-16 Hadley Black , Euiwoong Lee , Arya Mazumdar , Barna Saha

Quantum hardware suffers from intrinsic device heterogeneity and environmental drift, forcing practitioners to choose between suboptimal non-adaptive controllers or costly per-device recalibration. We derive a scaling law lower bound for…

Machine Learning · Computer Science 2026-05-21 Nima Leclerc , Chris Miller , Nicholas Brawand

This paper considers the problem of approximating a Boolean function $f$ using another Boolean function from a specified class. Two classes of approximating functions are considered: $k$-juntas, and linear Boolean functions. The $n$ input…

Information Theory · Computer Science 2019-07-09 Mohsen Heidari , S. Sandeep Pradhan , Ramji Venkataramanan