Gaussian dynamics in the double Siegel disk
- URL: http://arxiv.org/abs/2603.05824v2
- Date: Mon, 09 Mar 2026 04:15:59 GMT
- Title: Gaussian dynamics in the double Siegel disk
- Abstract summary: We show that deterministic multimode Gaussian channels admit a symmetric-space description.<n>This gives a bridge between covariance-matrix channel theory and the adjacency-matrix or symmetric-space picture.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We show that deterministic multimode Gaussian channels admit a symmetric-space description. Passing from the n-mode Siegel disk to a doubled version of that space lets general Gaussian dynamics act by a linear-fractional (Mobius) transformation on a single matrix parameter. This doubled disk naturally parametrizes Gaussian kernels in the Fock-Bargmann representation, and contains an explicit physical subset corresponding to valid mixed Gaussian states. Starting from the standard X,Y parametrization of a deterministic Gaussian channel, we construct a normalized oscillator-semigroup element whose fractional action reproduces the channel update on that subset; Gaussian unitaries appear as the symplectic, isometric special case. This gives a bridge between covariance-matrix channel theory and the adjacency-matrix or symmetric-space picture, preserves a simple composition law given by matrix multiplication of the acting blocks, and suggests a direct route to graphical update rules beyond pure states.
Related papers
- Uncovering Non-Gaussianity through Multi-Copy Symmetries [10.018030428478928]
We develop a group-theoretic multicopy approach to detect nonGaussianity.<n> Fixing the reference parameter purity, violationn identities certifies non-Gaussianity.<n>We show that relevant multi-copy detection can be estimated from bounded phase observables.
arXiv Detail & Related papers (2026-08-04T14:44:22Z) - Muon Dynamics as a Spectral Wasserstein Flow [21.689846521201588]
We introduce a family of Wasserstein norms on positive semidefinite matrices.<n>We prove the equivalence between the static and dynamic Benamou-Brenier formulations.<n>We identify exact gradientity associated with flow models.
arXiv Detail & Related papers (2026-04-06T17:41:12Z) - Regularized Online RLHF with Generalized Bilinear Preferences [68.44113000390544]
We consider the problem of contextual online RLHF with general preferences.<n>We adopt the Generalized Bilinear Preference Model to capture preferences via low-rank, skew-symmetric matrices.<n>We prove that the dual gap of the greedy policy is bounded by the square of the estimation error.
arXiv Detail & Related papers (2026-02-26T15:27:53Z) - Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation [56.361076943802594]
CanonFlow achieves state-of-the-art performance on the challenging GEOM-DRUG dataset, and the advantage remains large in few-step generation.
arXiv Detail & Related papers (2026-02-16T18:58:55Z) - Averaging of quantum channels via channel-state duality [0.0]
Twirling, uniform averaging over symmetry actions, is a standard tool for reducing the description of quantum states and channels to symmetry-invariant data.<n>We develop a framework for averaging quantum channels based on channel-state duality that converts pre- and post-processing averages into a group twirl acting directly on the Choi operator.
arXiv Detail & Related papers (2025-12-29T16:35:37Z) - Entanglement Sum Rule from Higher-Form Symmetries [0.0]
We prove an entanglement sum rule for $(d-1)$-dimensional quantum lattice models with finite abelian higher-form symmetries.<n>The framework explains and generalizes known examples in fermion-$mathbbZ$ gauge theory.
arXiv Detail & Related papers (2025-10-20T09:03:12Z) - Site Basis Excitation Ansatz for Matrix Product States [0.0]
We introduce a simple and efficient variation of the tangent-space excitation ansatz used to compute elementary excitation spectra of one-dimensional quantum lattice systems.<n>We show how one can construct Wannier excitations, analogous to the Wannier functions of band theory, where one Wannier excitation, translated to all sites, can reconstruct the single magnon modes exactly for all momenta.
arXiv Detail & Related papers (2025-09-07T23:05:55Z) - Efficient conversion from fermionic Gaussian states to matrix product states [48.225436651971805]
We propose a highly efficient algorithm that converts fermionic Gaussian states to matrix product states.<n>It can be formulated for finite-size systems without translation invariance, but becomes particularly appealing when applied to infinite systems.<n>The potential of our method is demonstrated by numerical calculations in two chiral spin liquids.
arXiv Detail & Related papers (2024-08-02T10:15:26Z) - Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry [63.694184882697435]
Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations.<n>This paper provides a comprehensive and unified understanding of the matrix logarithm and power from a Riemannian geometry perspective.
arXiv Detail & Related papers (2024-07-15T07:11:44Z) - Vectorization of the density matrix and quantum simulation of the von
Neumann equation of time-dependent Hamiltonians [65.268245109828]
We develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations.
We show that one of these linearizations of the von-Neumann equation corresponds to the standard case in which the state vector becomes the column stacked elements of the density matrix.
A quantum algorithm to simulate the dynamics of the density matrix is proposed.
arXiv Detail & Related papers (2023-06-14T23:08:51Z) - Riemannian optimization of photonic quantum circuits in phase and Fock space [4.601534909359792]
We propose a framework to design and optimize generic photonic quantum circuits composed of Gaussian objects.
We also make our framework extendable to non-Gaussian objects that can be written as linear combinations of Gaussian ones.
arXiv Detail & Related papers (2022-09-13T15:23:55Z) - Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [64.49871502193477]
We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix.
Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
arXiv Detail & Related papers (2022-05-21T01:47:17Z) - Riemannian statistics meets random matrix theory: towards learning from
high-dimensional covariance matrices [2.352645870795664]
This paper shows that there seems to exist no practical method of computing the normalising factors associated with Riemannian Gaussian distributions on spaces of high-dimensional covariance matrices.
It is shown that this missing method comes from an unexpected new connection with random matrix theory.
Numerical experiments are conducted which demonstrate how this new approximation can unlock the difficulties which have impeded applications to real-world datasets.
arXiv Detail & Related papers (2022-03-01T03:16:50Z) - Spectral clustering under degree heterogeneity: a case for the random
walk Laplacian [83.79286663107845]
This paper shows that graph spectral embedding using the random walk Laplacian produces vector representations which are completely corrected for node degree.
In the special case of a degree-corrected block model, the embedding concentrates about K distinct points, representing communities.
arXiv Detail & Related papers (2021-05-03T16:36:27Z) - Understanding Implicit Regularization in Over-Parameterized Single Index
Model [55.41685740015095]
We design regularization-free algorithms for the high-dimensional single index model.
We provide theoretical guarantees for the induced implicit regularization phenomenon.
arXiv Detail & Related papers (2020-07-16T13:27:47Z) - Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to
Heteroskedastic Noise [3.5429774642987915]
We show that the doubly-stochastic normalization of the Gaussian kernel with zero main diagonal is robust to heteroskedastic noise.
We provide examples of simulated and experimental single-cell RNA sequence data with intrinsic heteroskedasticity.
arXiv Detail & Related papers (2020-05-31T01:31:10Z) - Discrete aspects of continuous symmetries in the tensorial formulation
of Abelian gauge theories [0.0]
We show that standard identities and theorems for lattice models with $U(1)$ symmetry get re-expressed discretely.
We explain the geometrical analogy between the continuous lattice equations of motion and the discrete selection rules of the tensors.
We reformulate Noether's theorem for global, local, continuous or discrete Abelian symmetries.
arXiv Detail & Related papers (2020-03-24T17:26:26Z)
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.