Simulating extremal temporal correlations
- URL: http://arxiv.org/abs/2004.14854v2
- Date: Thu, 22 Oct 2020 08:49:55 GMT
- Title: Simulating extremal temporal correlations
- Abstract summary: correlations arising from sequential measurements on a single quantum system form a polytope.
This is defined by the arrow-of-time (AoT) constraints, meaning that future choices of measurement settings cannot influence past outcomes.
We discuss the resources needed to simulate the extreme points of the AoT polytope, where resources are quantified in terms of the minimal dimension, or "internal memory" of the physical system.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The correlations arising from sequential measurements on a single quantum
system form a polytope. This is defined by the arrow-of-time (AoT) constraints,
meaning that future choices of measurement settings cannot influence past
outcomes. We discuss the resources needed to simulate the extreme points of the
AoT polytope, where resources are quantified in terms of the minimal dimension,
or "internal memory" of the physical system. First, we analyze the equivalence
classes of the extreme points under symmetries. Second, we characterize the
minimal dimension necessary to obtain a given extreme point of the AoT
polytope, including a lower scaling bound in the asymptotic limit of long
sequences. Finally, we present a general method to derive dimension-sensitive
temporal inequalities for longer sequences, based on inequalities for shorter
ones, and investigate their robustness to imperfections.
Related papers
- Quantized Stochastic Primal-Dual Methods for Distributed Optimization under Relaxed Global Geometry [2.15242029196761]
We study distributed optimization with gradients and finite-bit communication modeled by random (unbiased) quantization.<n>Under restricted secant inequality (RSI), a constant step-size yields a linear contraction to an explicit neighborhood determined by gradient noise, quantization distortion, and network connectivity.<n>Under PolyakLojasiewicz (PL) inequality, we obtain linear-to-neighborhood convergence in the same quantized setting.
arXiv Detail & Related papers (2026-06-09T18:18:41Z) - A Mean-Field Analysis of Multi-Head Self-Attention under Cross-Entropy Training [0.0]
This paper develops a mean-field theory for a single-layer causal multi-head self-attention model trained by cross-entropy minimization.<n>In the infinite-head limit, the averaged attention logits define a risk functional on probability measures, whose first variation generates a nonlinear Wasserstein gradient-flow equation.<n>We study the long-time behavior of the PDE: energy dissipation, convergence to the stationary set under compactness, convergence to a single stationary measure under topological or Kurdyka--ojasiewicz assumptions, and explicit convergence rates under gradient-domination conditions.
arXiv Detail & Related papers (2026-06-09T06:38:27Z) - Online Riemannian Gradient Descent for Quantum State Tomography with Matrix Product Operators [8.817652993229068]
Matrix product operators (MPOs) provide a scalable approach for quantum state tomography (QST)<n>We study QST for quantum density matrices that can be represented by MPOs.<n>Motivated by this formulation, we develop an online gradient descent (oRGD) algorithm that sequentially incorporates measurement data.
arXiv Detail & Related papers (2026-05-06T06:19:46Z) - CLT-Optimal Parameter Error Bounds for Linear System Identification [15.678542274795412]
We show that the current state-of-the-art bounds do not accurately capture the statistical complexity of system identification.<n>Specifically, we utilize normality to identify classes of problem instances for which current bounds overstate the squared parameter error.<n>We then sharpen the OLS parameter error bounds via a novel second-order decomposition of the parameter error, where crucially the lower-order term is a matrix-valued martingale.
arXiv Detail & Related papers (2026-04-23T04:25:35Z) - Optimal Stopping in Latent Diffusion Models [22.471547966218278]
We identify and analyze a surprising phenomenon of Latent Diffusion Models (LDMs) where the final steps of the diffusion can degrade sample quality.<n>We provide a principled explanation by analyzing the interaction between latent dimension and stopping time.
arXiv Detail & Related papers (2025-10-09T16:28:48Z) - Finite-Time Information-Theoretic Bounds in Queueing Control [54.11376591632282]
We derive new policies that achieve them-for the total queue length in scheduling problems over processing networks with both adversarial and arrivals.<n>These findings reveal a fundamental limitation on "drift-only" methods and point the way toward principled, non-asymptotic optimality in queueing control.
arXiv Detail & Related papers (2025-06-23T04:14:40Z) - The suboptimality ratio of projective measurements restricted to low-rank subspaces [0.6345523830122168]
This paper theoretically examines the suboptimality arising from a Procrustes problem for minimizing the average distance between two fixed quantum states.
We show that the suboptimality ratio is independent of the dimension of the full space, and is at most polylogarithmic in the dimension of the low-rank subspace.
arXiv Detail & Related papers (2024-12-16T23:35:21Z) - Dimension reduction and the gradient flow of relative entropy [0.0]
Dimension reduction, widely used in science, maps high-dimensional data into low-dimensional space.
We investigate a basic mathematical model underlying the techniques of neighborhood embedding (SNE) and its popular variant t-SNE.
The aim is to map these points to low dimensions in an optimal way so that similar points are closer together.
arXiv Detail & Related papers (2024-09-25T14:23:04Z) - 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) - Learning with little mixing [9.574025613149871]
We study square loss in a realizable time-series framework with martingale difference noise.
Our main result is a fast rate excess risk bound which shows that whenever a trajectory hypercontractivity condition holds, the risk of the least-squares estimator on dependent data matches the iid rate order-wise after a burn-in time.
arXiv Detail & Related papers (2022-06-16T16:06:47Z) - Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector
Problems [98.34292831923335]
Motivated by the problem of online correlation analysis, we propose the emphStochastic Scaled-Gradient Descent (SSD) algorithm.
We bring these ideas together in an application to online correlation analysis, deriving for the first time an optimal one-time-scale algorithm with an explicit rate of local convergence to normality.
arXiv Detail & Related papers (2021-12-29T18:46:52Z) - 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) - Distinguishing Phases via Non-Markovian Dynamics of Entanglement in
Topological Quantum Codes under Parallel Magnetic Field [0.0]
Localizable entanglement is studied on nontrivial loops of topological quantum codes with parallel magnetic field.
We study the behavior of these lower bounds in the vicinity of the topological to nontopological quantum phase transition of the system.
We find that in the case of the non-Markovian dephasing noise, at large time, the canonical measurement-based lower bound oscillates with a larger amplitude.
arXiv Detail & Related papers (2021-08-25T12:23:40Z) - Nonparametric estimation of continuous DPPs with kernel methods [0.0]
Parametric and nonparametric inference methods have been proposed in the finite case, i.e. when the point patterns live in a finite ground set.
We show that a restricted version of this maximum likelihood (MLE) problem falls within the scope of a recent representer theorem for nonnegative functions in an RKHS.
We propose, analyze, and demonstrate a fixed point algorithm to solve this finite-dimensional problem.
arXiv Detail & Related papers (2021-06-27T11:57:14Z) - The Variational Method of Moments [65.91730154730905]
conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables.
Motivated by a variational minimax reformulation of OWGMM, we define a very general class of estimators for the conditional moment problem.
We provide algorithms for valid statistical inference based on the same kind of variational reformulations.
arXiv Detail & Related papers (2020-12-17T07:21:06Z) - Free Energy Wells and Overlap Gap Property in Sparse PCA [81.64027805404483]
We study a variant of the sparse PCA (principal component analysis) problem in the "hard" regime.
We show bounds on the depth of free energy wells for various Gibbs measures naturally associated to the problem.
We prove that the Overlap Gap Property (OGP) holds in a significant part of the hard regime.
arXiv Detail & Related papers (2020-06-18T17:18:02Z) - Structure of dimension-bounded temporal correlations [0.0]
We show that the temporal correlation space under constraints can be non generated.
We provide necessary and sufficient dimension a quantum system needed to generate a convex correlation space.
arXiv Detail & Related papers (2020-05-28T13:06:57Z) - On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and
Non-Asymptotic Concentration [115.1954841020189]
We study the inequality and non-asymptotic properties of approximation procedures with Polyak-Ruppert averaging.
We prove a central limit theorem (CLT) for the averaged iterates with fixed step size and number of iterations going to infinity.
arXiv Detail & Related papers (2020-04-09T17:54:18Z)
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.