Approximate Unitary $k$-Designs from Shallow, Low-Communication Circuits
- URL: http://arxiv.org/abs/2407.07876v2
- Date: Thu, 11 Jul 2024 17:56:53 GMT
- Title: Approximate Unitary $k$-Designs from Shallow, Low-Communication Circuits
- Authors: Nicholas LaRacuente, Felix Leditzky,
- Abstract summary: An approximate unitary $k$-design is an ensemble of unitaries and measure over which the average is close to a Haar random ensemble up to the first $k$ moments.
We construct multiplicative-error approximate unitary $k$-design ensembles for which communication between subsystems is $O(1)$ in the system size.
- Score: 6.844618776091756
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Random unitaries are useful in quantum information and related fields but hard to generate with limited resources. An approximate unitary $k$-design is an ensemble of unitaries and measure over which the average is close to a Haar (uniformly) random ensemble up to the first $k$ moments. A particularly strong notion of approximation bounds the distance from Haar randomness in relative error: the approximate design can be written as a convex combination involving an exact design and vice versa. We construct multiplicative-error approximate unitary $k$-design ensembles for which communication between subsystems is $O(1)$ in the system size. These constructions use the alternating projection method to analyze overlapping Haar twirls, giving a bound on the convergence speed to the full twirl with respect to the $2$-norm. Using von Neumann subalgebra indices to replace system dimension, the 2-norm distance converts to relative error without introducing any additional dimension dependence. Via recursion on these constructions, we construct a scheme yielding relative error designs in $O \big ( (k \log k + \log m + \log(1/\epsilon) ) k\, \text{polylog}(k) \big )$ depth, where $m$ is the number of qudits in the complete system and $\epsilon$ the approximation error. This sublinear depth construction answers one variant of [Harrow and Mehraban 2023, Open Problem 1]. Moreover, entanglement generated by the sublinear depth scheme follows area laws on spatial lattices up to corrections logarithmic in the full system size.
Related papers
- Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient Algorithms [65.42376001308064]
We propose two variance reduced ZO estimators for complex gradient problems.
We improve the state-of-the-art function complexities from $mathcalOleft(minfracdn1/2epsilon2, fracdepsilon3right)$ to $tildecalOleft(fracdepsilon2right)$.
arXiv Detail & Related papers (2024-10-03T15:04:01Z) - Relative-Translation Invariant Wasserstein Distance [82.6068808353647]
We introduce a new family of distances, relative-translation invariant Wasserstein distances ($RW_p$)
We show that $RW_p distances are also real distance metrics defined on the quotient set $mathcalP_p(mathbbRn)/sim$ invariant to distribution translations.
arXiv Detail & Related papers (2024-09-04T03:41:44Z) - Convergence of Unadjusted Langevin in High Dimensions: Delocalization of Bias [13.642712817536072]
We show that as the dimension $d$ of the problem increases, the number of iterations required to ensure convergence within a desired error increases.
A key technical challenge we address is the lack of a one-step contraction property in the $W_2,ellinfty$ metric to measure convergence.
arXiv Detail & Related papers (2024-08-20T01:24:54Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Optimal Approximation of Zonoids and Uniform Approximation by Shallow
Neural Networks [2.7195102129095003]
We study the following two related problems.
The first is to determine what error an arbitrary zonoid in $mathbbRd+1$ can be approximated in the Hausdorff distance by a sum of $n$ line segments.
The second is to determine optimal approximation rates in the uniform norm for shallow ReLU$k$ neural networks on their variation spaces.
arXiv Detail & Related papers (2023-07-28T03:43:17Z) - Pseudonorm Approachability and Applications to Regret Minimization [73.54127663296906]
We convert high-dimensional $ell_infty$-approachability problems to low-dimensional pseudonorm approachability problems.
We develop an algorithmic theory of pseudonorm approachability, analogous to previous work on approachability for $ell$ and other norms.
arXiv Detail & Related papers (2023-02-03T03:19:14Z) - Random quantum circuits transform local noise into global white noise [118.18170052022323]
We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime.
For local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution $p_textnoisy$ of a generic noisy circuit instance shrink exponentially.
If the noise is incoherent, the output distribution approaches the uniform distribution $p_textunif$ at precisely the same rate.
arXiv Detail & Related papers (2021-11-29T19:26:28Z) - Complexity of zigzag sampling algorithm for strongly log-concave
distributions [6.336005544376984]
We study the computational complexity of zigzag sampling algorithm for strongly log-concave distributions.
We prove that the zigzag sampling algorithm achieves $varepsilon$ error in chi-square divergence with a computational cost equivalent to $Obigl.
arXiv Detail & Related papers (2020-12-21T03:10:21Z) - Optimal Robust Linear Regression in Nearly Linear Time [97.11565882347772]
We study the problem of high-dimensional robust linear regression where a learner is given access to $n$ samples from the generative model $Y = langle X,w* rangle + epsilon$
We propose estimators for this problem under two settings: (i) $X$ is L4-L2 hypercontractive, $mathbbE [XXtop]$ has bounded condition number and $epsilon$ has bounded variance and (ii) $X$ is sub-Gaussian with identity second moment and $epsilon$ is
arXiv Detail & Related papers (2020-07-16T06:44:44Z)
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.