Entropy Stability and Spectral Concentration under Convex Block Constraints
- URL: http://arxiv.org/abs/2512.16192v1
- Date: Wed, 17 Dec 2025 14:14:27 GMT
- Title: Entropy Stability and Spectral Concentration under Convex Block Constraints
- Authors: Hassan Nasreddine,
- Abstract summary: We investigate entropy minimization problems for quantum states subject to convex block-diagonal constraints.<n>If a state has entropy within epsilon of the minimum possible value under a fixed block constraint, then it must lie within O(epsilon1/2) in trace norm of the manifold of entropy minimizers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate entropy minimization problems for quantum states subject to convex block-diagonal constraints. Our principal result is a quantitative stability theorem: if a state has entropy within epsilon of the minimum possible value under a fixed block constraint, then it must lie within O(epsilon^{1/2}) in trace norm of the manifold of entropy minimizers. We show that this rate is optimal. The analysis is entirely finite-dimensional and relies on a precise decomposition of entropy into classical and internal components, together with sharp relative entropy inequalities. As an application, we study finite additive operators whose spectral decomposition induces natural block constraints. In this setting, the stability theorem yields quantitative non-concentration bounds for induced spectral measures. The framework is abstract and independent of arithmetic input. It provides a general stability principle for entropy minimizers under linear spectral constraints.
Related papers
- Harmonic rigidity at fixed spectral gap in one dimension [0.0]
We prove that the harmonic trap is the unique maximizer of the ground-state position variance.<n>We obtain a sharp geometric quantum speed-limit bound on the position-position component of the quantum metric.
arXiv Detail & Related papers (2025-12-31T11:20:31Z) - Stability of Maximum-Entropy Inference in Finite Dimensions [0.0]
We study maximum-entropy inference for finite-dimensional quantum states under linear moment constraints.<n>We prove that convergence of moments and entropy implies convergence of states in trace norm.
arXiv Detail & Related papers (2025-10-24T02:13:48Z) - Asymptotic Exceptional Steady States in Dissipative Dynamics [0.0]
Spectral degeneracies in Liouvillian generators of dissipative dynamics generically occur as exceptional points, where the corresponding non-Hermitian operator becomes non-diagonal.<n>We show that exceptional steady states at the physical value $W=1$ may be understood as a critical point hallmarking the onset of dynamical instability.
arXiv Detail & Related papers (2025-04-03T18:00:02Z) - Nonstabilizerness in U(1) lattice gauge theory [0.0]
Nonstabilizerness is a fundamental quantum resource that quantifies state complexity within the framework of quantum computing.<n>We show how nonstabilizerness is always extensive with volume, and has no direct relation to the presence of critical points.<n>Our results indicate that error-corrected simulations of lattice gauge theories close to the limit continuum have similar computational costs to those at finite correlation length.
arXiv Detail & Related papers (2024-09-03T11:09:01Z) - Entropy Constraints for Ground Energy Optimization [10.2138250640885]
We study the use of von Neumann entropy constraints for obtaining lower bounds on the ground energy of quantum many-body systems.
Known methods for obtaining certificates on the ground energy typically use consistency of local observables and are expressed as semidefinite programming relaxations.
arXiv Detail & Related papers (2023-05-11T14:51:21Z) - Role of boundary conditions in the full counting statistics of
topological defects after crossing a continuous phase transition [62.997667081978825]
We analyze the role of boundary conditions in the statistics of topological defects.
We show that for fast and moderate quenches, the cumulants of the kink number distribution present a universal scaling with the quench rate.
arXiv Detail & Related papers (2022-07-08T09:55:05Z) - Global Convergence of Over-parameterized Deep Equilibrium Models [52.65330015267245]
A deep equilibrium model (DEQ) is implicitly defined through an equilibrium point of an infinite-depth weight-tied model with an input-injection.
Instead of infinite computations, it solves an equilibrium point directly with root-finding and computes gradients with implicit differentiation.
We propose a novel probabilistic framework to overcome the technical difficulty in the non-asymptotic analysis of infinite-depth weight-tied models.
arXiv Detail & Related papers (2022-05-27T08:00:13Z) - Tight Exponential Analysis for Smoothing the Max-Relative Entropy and
for Quantum Privacy Amplification [56.61325554836984]
The max-relative entropy together with its smoothed version is a basic tool in quantum information theory.
We derive the exact exponent for the decay of the small modification of the quantum state in smoothing the max-relative entropy based on purified distance.
arXiv Detail & Related papers (2021-11-01T16:35:41Z) - Optimal policy evaluation using kernel-based temporal difference methods [78.83926562536791]
We use kernel Hilbert spaces for estimating the value function of an infinite-horizon discounted Markov reward process.
We derive a non-asymptotic upper bound on the error with explicit dependence on the eigenvalues of the associated kernel operator.
We prove minimax lower bounds over sub-classes of MRPs.
arXiv Detail & Related papers (2021-09-24T14:48:20Z) - Exact many-body scars and their stability in constrained quantum chains [55.41644538483948]
Quantum scars are non-thermal eigenstates characterized by low entanglement entropy.
We study the response of these exact quantum scars to perturbations by analysing the scaling of the fidelity susceptibility with system size.
arXiv Detail & Related papers (2020-11-16T19:05:50Z) - Fine-Grained Analysis of Stability and Generalization for Stochastic
Gradient Descent [55.85456985750134]
We introduce a new stability measure called on-average model stability, for which we develop novel bounds controlled by the risks of SGD iterates.
This yields generalization bounds depending on the behavior of the best model, and leads to the first-ever-known fast bounds in the low-noise setting.
To our best knowledge, this gives the firstever-known stability and generalization for SGD with even non-differentiable loss functions.
arXiv Detail & Related papers (2020-06-15T06:30:19Z)
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.