Unitarity estimation for quantum channels
- URL: http://arxiv.org/abs/2212.09319v4
- Date: Tue, 9 May 2023 15:14:33 GMT
- Title: Unitarity estimation for quantum channels
- Authors: Kean Chen, Qisheng Wang, Peixun Long, Mingsheng Ying
- Abstract summary: Unitarity estimation is a basic and important problem in quantum device certification and benchmarking.
We provide a unified framework for unitarity estimation, which induces ancilla-efficient algorithms.
We show that both the $d$-dependence and $epsilon$-dependence of our algorithms are optimal.
- Score: 7.323367190336826
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Estimating the unitarity of an unknown quantum channel $\mathcal{E}$ provides
information on how much it is unitary, which is a basic and important problem
in quantum device certification and benchmarking. Unitarity estimation can be
performed with either coherent or incoherent access, where the former in
general leads to better query complexity while the latter allows more practical
implementations. In this paper, we provide a unified framework for unitarity
estimation, which induces ancilla-efficient algorithms that use
$O(\epsilon^{-2})$ and $O(\sqrt{d}\cdot\epsilon^{-2})$ calls to $\mathcal{E}$
with coherent and incoherent accesses, respectively, where $d$ is the dimension
of the system that $\mathcal{E}$ acts on and $\epsilon$ is the required
precision. We further show that both the $d$-dependence and
$\epsilon$-dependence of our algorithms are optimal. As part of our results, we
settle the query complexity of the distinguishing problem for depolarizing and
unitary channels with incoherent access by giving a matching lower bound
$\Omega(\sqrt{d})$, improving the prior best lower bound $\Omega(\sqrt[3]{d})$
by Aharonov et al. (Nat. Commun. 2022) and Chen et al. (FOCS 2021).
Related papers
- Quantum spectral method for gradient and Hessian estimation [4.193480001271463]
Gradient descent is one of the most basic algorithms for solving continuous optimization problems.
We propose a quantum algorithm that returns an $varepsilon$-approximation of its gradient with query complexity $widetildeO (1/varepsilon)$.
We also propose two quantum algorithms for Hessian estimation, aiming to improve quantum analogs of Newton's method.
arXiv Detail & Related papers (2024-07-04T11:03:48Z) - Towards Optimal Circuit Size for Sparse Quantum State Preparation [10.386753939552872]
We consider the preparation for $n$-qubit sparse quantum states with $s$ non-zero amplitudes and propose two algorithms.
The first algorithm uses $O(ns/log n + n)$ gates, improving upon previous methods by $O(log n)$.
The second algorithm is tailored for binary strings that exhibit a short Hamiltonian path.
arXiv Detail & Related papers (2024-04-08T02:13:40Z) - The Computational Complexity of Finding Stationary Points in Non-Convex Optimization [53.86485757442486]
Finding approximate stationary points, i.e., points where the gradient is approximately zero, of non-order but smooth objective functions is a computational problem.
We show that finding approximate KKT points in constrained optimization is reducible to finding approximate stationary points in unconstrained optimization but the converse is impossible.
arXiv Detail & Related papers (2023-10-13T14:52:46Z) - Near-Optimal Bounds for Learning Gaussian Halfspaces with Random
Classification Noise [50.64137465792738]
We show that any efficient SQ algorithm for the problem requires sample complexity at least $Omega(d1/2/(maxp, epsilon)2)$.
Our lower bound suggests that this quadratic dependence on $1/epsilon$ is inherent for efficient algorithms.
arXiv Detail & Related papers (2023-07-13T18:59:28Z) - Succinct quantum testers for closeness and $k$-wise uniformity of probability distributions [2.3466828785520373]
We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions.
We show that the quantum query complexities for $ell1$- and $ell2$-closeness testing are $O(sqrtn/varepsilon)$ and $O(sqrtnk/varepsilon)$.
We propose the first quantum algorithm for this problem with query complexity $O(sqrtnk/varepsilon)
arXiv Detail & Related papers (2023-04-25T15:32:37Z) - Quantum Resources Required to Block-Encode a Matrix of Classical Data [56.508135743727934]
We provide circuit-level implementations and resource estimates for several methods of block-encoding a dense $Ntimes N$ matrix of classical data to precision $epsilon$.
We examine resource tradeoffs between the different approaches and explore implementations of two separate models of quantum random access memory (QRAM)
Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
arXiv Detail & Related papers (2022-06-07T18:00:01Z) - 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) - 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) - Agnostic Q-learning with Function Approximation in Deterministic
Systems: Tight Bounds on Approximation Error and Sample Complexity [94.37110094442136]
We study the problem of agnostic $Q$-learning with function approximation in deterministic systems.
We show that if $delta = Oleft(rho/sqrtdim_Eright)$, then one can find the optimal policy using $Oleft(dim_Eright)$.
arXiv Detail & Related papers (2020-02-17T18:41:49Z)
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.