Global Minimax Readout of a Qubit Direction
- URL: http://arxiv.org/abs/2608.16840v1
- Date: Mon, 17 Aug 2026 17:26:32 GMT
- Title: Global Minimax Readout of a Qubit Direction
- Abstract summary: We determine the exact worst-direction Fisher-information cost of using a parameter-independent readout to estimate an unknown qubit direction at known Bloch-vector length $$.<n>Every fixed-local architecture, including recorded classical randomization, reduces exactly to a zero-barycenter probability measure on the Bloch ball.<n>For every full-rank qubit and every trace-balanced spectral Fisher loss, the resulting minimax problem is rigid.
- Score: 5.246986428523558
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We determine the exact worst-direction Fisher-information cost of using a parameter-independent readout to estimate an unknown qubit direction at known Bloch-vector length $η$. Every fixed-local architecture, including recorded classical randomization, heterogeneous single-copy measurements, and arbitrary outcome spaces, reduces exactly to a zero-barycenter probability measure on the Bloch ball. For every full-rank qubit and every trace-balanced spectral Fisher loss, the resulting minimax problem is rigid: the unique optimal aggregate design is the spin-coherent Haar positive-operator-valued measure (POVM). For $N$ copies, inverse-Fisher $A$ loss has the exact value $2/[Nf(η)]$, where $ f(η)= \frac{2η-(1-η^2)\log[(1+η)/(1-η)]}{4η}$. This uniqueness has an immediate finite-readout consequence. No finite-support measurement attains the unrestricted mixed-state optimum, while at the smallest globally regular support the tetrahedral symmetric informationally complete (SIC) measurement is uniquely $A$- and $D$-minimax, with exact worst-direction values. Relaxing the fixed-readout constraint separates the asymptotic resources: one-way local operations and classical communication (LOCC), unrestricted LOCC, and separable measurements have $A$-loss coefficient $4/η^2$, whereas collective measurements attain $2(1+η)/η^2$. We further classify the rigidity conditions for unequal contrasts and show that Haar uniqueness survives at the nonregular pure-state endpoint.
Related papers
- The exact price of local realism in CHSH experiments: a measurement-dependence-detection trade-off surface, a moiré phase-locking mechanism that saturates it, and an unmeasured fringe in the fourfold coincidence sum [0.0]
I solve the unique detection profile $D(m)=sqrtm,h(m)$ under which a deterministic sign model reproduces the singlet exactly.<n>Surviving local accounts trade off measurement dependence against a $cos 4(a-b)$ modulation of the fourfold coincidence sum.<n>I exhibit a local mechanism -- moire phase locking -- with deterministic phase evolution and all randomness quenched in frozen offsets and flight times.
arXiv Detail & Related papers (2026-08-19T13:14:52Z) - Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids: From Robust Offline Optimization to Full-Bandit Learning [55.29259818039367]
We study the nonnegative submodular subject to a general matroid when the offline algorithm is given an arbitrary controlled value oracle.<n>Our algorithm retains limiting approximation factors $1/e$ for non-monotone objectives and $1-1/e$ for monotone objectives.<n>As a consequence, the offline-to-online reduction yields single-bandit CMAB algorithms for general matroid-constrained submodular rewards.
arXiv Detail & Related papers (2026-08-12T14:54:15Z) - Uniformly Stable Minimal Weyl--Heisenberg Measurements Approaching the SIC Benchmark [2.5735476569508995]
Informational completeness (IC) guarantees that an inverse exists, not that it is statistically well conditioned.<n>For minimal rank-one Weyl--Heisenberg measurements, covariance makes the nonidentity projector-Gram spectrum proportional to the fiducial's ambiguity intensities.<n>A balanced one-coordinate perturbation repairs the zero ambiguity axis of a cubic Alltop state, gives an attained floor uniformly bounded below by a positive constant, and confines the entire nonidentity spectrum to ([L_q,U_q]) with (U_q/L_qto1
arXiv Detail & Related papers (2026-08-12T09:41:26Z) - Demixing Sparse Signals from Nonlinear Observations using Generalized Non-convex Regularization [0.0]
We consider the recovery of a pair of sparse vectors from a limited number of nonlinear observations of their superposition.<n>We propose a regularization-based framework combining a Huberized data fidelity with generalized folded-concave penalties.<n>We derive estimation error bounds of order $sqrtslog(n)/m$ that hold at emphevery localized stationary point.
arXiv Detail & Related papers (2026-07-12T07:29:39Z) - SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums [51.49970814177172]
Empirical risk on datasets naturally exhibits similarities in $N=nm$ total samples.<n>We provide an analysis that avoids pessimistic convergence analysis.<n>Our results improve existing state-of-the-art regimes.
arXiv Detail & Related papers (2026-06-14T14:11:07Z) - Tight Convergence Rates for Online Distributed Linear Estimation with Adversarial Measurements [66.94250413799232]
We study mean estimation of a random vector $X$ in a distributed parameter-server-worker setup.<n>Key challenges are adversarial measurements and asynchrony.<n>Results provide a unified finite-time characterization of robustness, identifiability, and statistical efficiency in distributed linear estimation with adversarial workers.
arXiv Detail & Related papers (2026-04-07T11:45:55Z) - 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) - Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning [61.07540493350384]
Self-distillation (SD) is the process of retraining a student on a mixture of ground-truth and the teacher's own predictions.<n>We show that for any prediction risk, the optimally mixed student improves upon the ridge teacher for every regularization level.<n>We propose a consistent one-shot tuning method to estimate $star$ without grid search, sample splitting, or refitting.
arXiv Detail & Related papers (2026-02-19T17:21:15Z) - Precise Asymptotics of Bagging Regularized M-estimators [20.077783679095443]
We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators.<n>Key to our analysis is a new result on the joint behavior of correlations between the estimator and residual errors on overlapping subsamples.<n>Joint optimization of subsample size, ensemble size, and regularization can significantly outperform regularizer optimization alone on the full data.
arXiv Detail & Related papers (2024-09-23T17:48:28Z) - Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic
Shortest Path [80.60592344361073]
We study the Shortest Path (SSP) problem with a linear mixture transition kernel.
An agent repeatedly interacts with a environment and seeks to reach certain goal state while minimizing the cumulative cost.
Existing works often assume a strictly positive lower bound of the iteration cost function or an upper bound of the expected length for the optimal policy.
arXiv Detail & Related papers (2024-02-14T07:52:00Z) - Semidefinite programming relaxations and debiasing for MAXCUT-based clustering [1.9761774213809036]
We consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of 2 sub-gaussian distributions in $mathbbRp$.<n>We use semidefinite programming relaxations of an integer quadratic program that is formulated as finding the maximum cut on a graph.
arXiv Detail & Related papers (2024-01-16T03:14:24Z)
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.