An efficient quantum algorithm for computing $S$-units and its applications
- URL: http://arxiv.org/abs/2510.02280v1
- Date: Thu, 02 Oct 2025 17:54:24 GMT
- Title: An efficient quantum algorithm for computing $S$-units and its applications
- Abstract summary: We provide details on the proofs of the quantum time algorithm of Biasse and Song (SODA 16) for computing the $S$-unit group of a number group.
- Score: 0.3867363075280543
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we provide details on the proofs of the quantum polynomial time algorithm of Biasse and Song (SODA 16) for computing the $S$-unit group of a number field. This algorithm directly implies polynomial time methods to calculate class groups, S-class groups, relative class group and the unit group, ray class groups, solve the principal ideal problem, solve certain norm equations, and decompose ideal classes in the ideal class group. Additionally, combined with a result of Cramer, Ducas, Peikert and Regev (Eurocrypt 2016), the resolution of the principal ideal problem allows one to find short generators of a principal ideal. Likewise, methods due to Cramer, Ducas and Wesolowski (Eurocrypt 2017) use the resolution of the principal ideal problem and the decomposition of ideal classes to find so-called ``mildly short vectors'' in ideal lattices of cyclotomic fields.
Related papers
- Fundamental limits of distributed multiclass classification from simple binary decisions [5.668668551176212]
We study the fundamental performance limits of such a classifier when the corresponding binary classifiers are hyperplanes.<n>For a stylized Gaussian setting where the $K$ class centers are independent Gaussian points in $mathbb Rd$ and the observations are corrupted by Gaussian noise, we derive explicit performance bounds across several decoding and dimensional regimes.
arXiv Detail & Related papers (2026-07-21T17:53:44Z) - Provably Adaptive Linear Approximation for the Shapley Value and Beyond [73.0940890296463]
A fundamental and long-standing challenge is their efficient approximation.<n>We develop a linear-space algorithm that requires $P(|hatboldsymbol-boldsymbol|_2geq)leq $ for all commonly used semi-values.<n>Our algorithm allows explicit minimization of the mean square error for each specific utility function.
arXiv Detail & Related papers (2026-04-09T16:38:14Z) - Low-degree Lower bounds for clustering in moderate dimension [53.03724383992195]
We study the fundamental problem of clustering $n$ points into $K$ groups drawn from a mixture of isotropic Gaussians in $mathbbRd$.<n>We show that while the difficulty of clustering for $n leq dK$ is driven by dimension reduction and spectral methods, the moderate-dimensional regime involves more delicate phenomena leading to a "non-optimal rate"<n>We provide a novel non-spectral algorithm matching this rate, shedding new light on the computational limits of the clustering problem in moderate dimension.
arXiv Detail & Related papers (2026-02-26T14:03:55Z) - Upper bounding the quantum space complexity for computing class group and principal ideal problem [0.0]
We calculate the upper bound on quantum space complexity of the quantum algorithms proposed by Biasse and Song.
We follow the approach of Barbulescu and Poulalion and the framework given by de Boer, Ducas, and Fehr.
arXiv Detail & Related papers (2024-05-21T05:37:04Z) - Efficiently Learning One-Hidden-Layer ReLU Networks via Schur
Polynomials [50.90125395570797]
We study the problem of PAC learning a linear combination of $k$ ReLU activations under the standard Gaussian distribution on $mathbbRd$ with respect to the square loss.
Our main result is an efficient algorithm for this learning task with sample and computational complexity $(dk/epsilon)O(k)$, whereepsilon>0$ is the target accuracy.
arXiv Detail & Related papers (2023-07-24T14:37:22Z) - Embedding Integer Lattices as Ideals into Polynomial Rings [29.240943119083678]
We study the additional structure of ideal lattices further and find that a given ideal lattice in a ring can be embedded as an ideal into infinitely many different rings by embedding the coefficient.
We find a flaw in Ding and Lindner's algorithm that causes some ideal lattices can't be identified by their algorithm.
arXiv Detail & Related papers (2023-07-24T03:06:49Z) - Efficient Convex Algorithms for Universal Kernel Learning [46.573275307034336]
An ideal set of kernels should: admit a linear parameterization (for tractability); dense in the set of all kernels (for accuracy)
Previous algorithms for optimization of kernels were limited to classification and relied on computationally complex Semidefinite Programming (SDP) algorithms.
We propose a SVD-QCQPQP algorithm which dramatically reduces the computational complexity as compared with previous SDP-based approaches.
arXiv Detail & Related papers (2023-04-15T04:57:37Z) - Private estimation algorithms for stochastic block models and mixture
models [63.07482515700984]
General tools for designing efficient private estimation algorithms.
First efficient $(epsilon, delta)$-differentially private algorithm for both weak recovery and exact recovery.
arXiv Detail & Related papers (2023-01-11T09:12:28Z) - An efficient quantum algorithm for lattice problems achieving
subexponential approximation factor [2.3351527694849574]
We give a quantum algorithm for solving the Bounded Distance Decoding (BDD) problem with a subexponential approximation factor on a class of integer lattices.
The running time of the quantum algorithm is for one range of approximation factors and subexponential time for a second range of approximation factors.
This view makes for a clean quantum algorithm in terms of finite abelian groups, uses relatively little from lattice theory, and suggests exploring approximation algorithms for lattice problems in parameters other than dimension alone.
arXiv Detail & Related papers (2022-01-31T18:58:33Z) - Lattice-Based Methods Surpass Sum-of-Squares in Clustering [98.46302040220395]
Clustering is a fundamental primitive in unsupervised learning.
Recent work has established lower bounds against the class of low-degree methods.
We show that, perhaps surprisingly, this particular clustering model textitdoes not exhibit a statistical-to-computational gap.
arXiv Detail & Related papers (2021-12-07T18:50:17Z) - Generation and frame characteristics of predefined evenly-distributed
class centroids for pattern classification [4.129225533930966]
This paper takes advantage of regular polyhedron in high-dimensional space and the evenly distribution of points on the n dimensional hypersphere to generate PEDCC mathematically.
Experiments show that new algorithm is not only faster than the iterative method, but also more accurate in position.
arXiv Detail & Related papers (2021-05-02T06:35:51Z) - Certifying clusters from sum-of-norms clustering [13.747619681451875]
We present a clustering test that identifies and certifies the correct cluster assignment from an approximate solution.
We show the correct cluster assignment is guaranteed to be certified by a primal-dual path following algorithm after sufficiently many iterations.
arXiv Detail & Related papers (2020-06-19T20:26:26Z) - Robustly Learning any Clusterable Mixture of Gaussians [55.41573600814391]
We study the efficient learnability of high-dimensional Gaussian mixtures in the adversarial-robust setting.
We provide an algorithm that learns the components of an $epsilon$-corrupted $k$-mixture within information theoretically near-optimal error proofs of $tildeO(epsilon)$.
Our main technical contribution is a new robust identifiability proof clusters from a Gaussian mixture, which can be captured by the constant-degree Sum of Squares proof system.
arXiv Detail & Related papers (2020-05-13T16:44:12Z) - Optimal Randomized First-Order Methods for Least-Squares Problems [56.05635751529922]
This class of algorithms encompasses several randomized methods among the fastest solvers for least-squares problems.
We focus on two classical embeddings, namely, Gaussian projections and subsampled Hadamard transforms.
Our resulting algorithm yields the best complexity known for solving least-squares problems with no condition number dependence.
arXiv Detail & Related papers (2020-02-21T17:45:32Z)
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.