Classical shadows with arbitrary group representations
- URL: http://arxiv.org/abs/2604.01429v1
- Date: Wed, 01 Apr 2026 22:09:44 GMT
- Title: Classical shadows with arbitrary group representations
- Abstract summary: Classical shadows (CS) has emerged as an important framework to efficiently predict properties of an unknown quantum state.<n>We present a unified theory that allows us to simultaneously understand CS protocols based on sampling from general group representations.<n>Our method allows us to readily generate new protocols based on other groups, or different representations of previously considered ones.
- Score: 0.43340169930181155
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Classical shadows (CS) has recently emerged as an important framework to efficiently predict properties of an unknown quantum state. A common strategy in CS protocols is to parametrize the basis in which one measures the state by a random group action; many examples of this have been proposed and studied on a case-by-case basis. In this work, we present a unified theory that allows us to simultaneously understand CS protocols based on sampling from general group representations, extending previous approaches that worked in simplified (multiplicity-free) settings. We identify a class of measurement bases which we call "centralizing bases" that allows us to analytically characterize and invert the measurement channel, minimizing classical post-processing costs. We complement this analysis by deriving general bounds on the sample-complexity necessary to obtain estimates of a given precision. Beyond its unification of previous CS protocols, our method allows us to readily generate new protocols based on other groups, or different representations of previously considered ones. For example, we characterize novel shadow protocols based on sampling from the spin and tensor representations of $\textsf{SU}(2)$, symmetric and orthogonal groups, and the exceptional Lie group $G_2$.
Related papers
- Classical shadows over symmetric spaces [0.0]
We study the classical shadow protocols occasioned by sampling uniformly randomly from a compact group.<n>We uncover a unifying theory of such protocols, extending the extent to which the general theory of classical shadows is understood at a mathematical level.
arXiv Detail & Related papers (2026-05-06T23:34:13Z) - An Online Approach for Entanglement Verification Using Classical Shadows [1.7003745126517356]
We propose an alternative formulation of classical estimators as online algorithms that are updated incrementally upon obtaining a new sample.<n>Online estimator certifies entanglement reliably and, by exploiting all $binomTm$ combinations of snapshots, requires fewer samples than state-of-the-art baselines.
arXiv Detail & Related papers (2026-03-27T16:59:14Z) - Learnable Chernoff Baselines for Inference-Time Alignment [64.81256817158851]
We introduce Learnable Chernoff Baselines as a method for efficiently and approximately sampling from exponentially tilted kernels.<n>We establish total-variation guarantees to the ideal aligned model, and demonstrate in both continuous and discrete diffusion settings that LCB sampling closely matches ideal rejection sampling.
arXiv Detail & Related papers (2026-02-08T00:09:40Z) - Unified Framework for Matchgate Classical Shadows [0.0]
Estimating quantum fermionic properties is a computationally difficult yet crucial task for the study of electronic systems.
Recent developments have begun to address this challenge by introducing classical shadows protocols.
We propose an approach that unifies these different protocols, proving their equivalence, and deriving from it an optimal sampling scheme.
arXiv Detail & Related papers (2024-09-05T18:01:00Z) - Improved classical shadows from local symmetries in the Schur basis [4.462208715451194]
We study the sample complexity of the classical shadows task.
We present the first joint measurement protocol for classical shadows whose sample complexity scales with the rank of the unknown state.
arXiv Detail & Related papers (2024-05-15T17:33:10Z) - Efficient Local Classical Shadow Tomography with Number Conservation [0.0]
Shadow tomography aims to build a classical description of a quantum state from a sequence of simple random measurements.
We propose and analyze a new local shadow protocol adapted to systems with fundamental number conservation laws, such as ultracold atoms.
arXiv Detail & Related papers (2023-11-15T19:00:01Z) - Structured Voronoi Sampling [61.629198273926676]
In this paper, we take an important step toward building a principled approach for sampling from language models with gradient-based methods.
We name our gradient-based technique Structured Voronoi Sampling (SVS)
In a controlled generation task, SVS is able to generate fluent and diverse samples while following the control targets significantly better than other methods.
arXiv Detail & Related papers (2023-06-05T17:32:35Z) - Joint Bayesian Inference of Graphical Structure and Parameters with a
Single Generative Flow Network [59.79008107609297]
We propose in this paper to approximate the joint posterior over the structure of a Bayesian Network.
We use a single GFlowNet whose sampling policy follows a two-phase process.
Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models.
arXiv Detail & Related papers (2023-05-30T19:16:44Z) - Sensing Cox Processes via Posterior Sampling and Positive Bases [56.82162768921196]
We study adaptive sensing of point processes, a widely used model from spatial statistics.
We model the intensity function as a sample from a truncated Gaussian process, represented in a specially constructed positive basis.
Our adaptive sensing algorithms use Langevin dynamics and are based on posterior sampling (textscCox-Thompson) and top-two posterior sampling (textscTop2) principles.
arXiv Detail & Related papers (2021-10-21T14:47:06Z) - Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency)
Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
arXiv Detail & Related papers (2021-03-24T07:26:07Z) - Revisiting the Sample Complexity of Sparse Spectrum Approximation of
Gaussian Processes [60.479499225746295]
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space.
Our approximation is obtained from an improved sample complexity analysis for sparse spectrum Gaussian processes (SSGPs)
arXiv Detail & Related papers (2020-11-17T05:41:50Z) - Regularized spectral methods for clustering signed networks [7.547306670351599]
We study the problem of $k$-way clustering in signed graphs.
We build on the results in [CDGT 2019] on two fronts for the normalized versions of SPONGE and the Signed Laplacian.
arXiv Detail & Related papers (2020-11-03T14:40:34Z)
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.