Non-representable quantum measures
- URL: http://arxiv.org/abs/2508.14326v1
- Date: Wed, 20 Aug 2025 00:47:24 GMT
- Title: Non-representable quantum measures
- Authors: Alexandru Chirvasitu,
- Abstract summary: Grade-$d$ measures on a $sigma$-algebra $mathcalAsubseteq 2X$ over a set $X$ are generalizations of measures satisfying one of a hierarchy of weak additivity-type conditions.<n>Every signed polymeasure $lambda$ on $(X,mathcalA)d$ produces a grade-$d$ measure as its diagonal $widetildelambda(A):=lambda(A,cdots,A)$.
- Score: 55.2480439325792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Grade-$d$ measures on a $\sigma$-algebra $\mathcal{A}\subseteq 2^X$ over a set $X$ are generalizations of measures satisfying one of a hierarchy of weak additivity-type conditions initially introduced as interference operators in quantum mechanics. Every signed polymeasure $\lambda$ on $(X,\mathcal{A})^d$ produces a grade-$d$ measure as its diagonal $\widetilde{\lambda}(A):=\lambda(A,\cdots,A)$, and we prove that as soon as $d\ge 2$ measures (as opposed to polymeasures) do not suffice: the separate $\sigma$-additivity of a $\lambda$ producing $\mu=\widetilde{\lambda}$ cannot, generally, be amplified to global $\sigma$-additivity. This amends a result in the literature, asserting the contrary in case $d=2$.
Related papers
- Approximating the operator norm of local Hamiltonians via few quantum states [53.16156504455106]
Consider a Hermitian operator $A$ acting on a complex Hilbert space of $2n$.<n>We show that when $A$ has small degree in the Pauli expansion, or in other words, $A$ is a local $n$-qubit Hamiltonian.<n>We show that whenever $A$ is $d$-local, textiti.e., $deg(A)le d$, we have the following discretization-type inequality.
arXiv Detail & Related papers (2025-09-15T14:26:11Z) - The Communication Complexity of Approximating Matrix Rank [50.6867896228563]
We show that this problem has randomized communication complexity $Omega(frac1kcdot n2log|mathbbF|)$.
As an application, we obtain an $Omega(frac1kcdot n2log|mathbbF|)$ space lower bound for any streaming algorithm with $k$ passes.
arXiv Detail & Related papers (2024-10-26T06:21:42Z) - Dimension Independent Disentanglers from Unentanglement and Applications [55.86191108738564]
We construct a dimension-independent k-partite disentangler (like) channel from bipartite unentangled input.
We show that to capture NEXP, it suffices to have unentangled proofs of the form $| psi rangle = sqrta | sqrt1-a | psi_+ rangle where $| psi_+ rangle has non-negative amplitudes.
arXiv Detail & Related papers (2024-02-23T12:22:03Z) - Distribution-Independent Regression for Generalized Linear Models with
Oblivious Corruptions [49.69852011882769]
We show the first algorithms for the problem of regression for generalized linear models (GLMs) in the presence of additive oblivious noise.
We present an algorithm that tackles newthis problem in its most general distribution-independent setting.
This is the first newalgorithmic result for GLM regression newwith oblivious noise which can handle more than half the samples being arbitrarily corrupted.
arXiv Detail & Related papers (2023-09-20T21:41:59Z) - Parameterized multipartite entanglement measures [2.4172837625375]
We present two types of entanglement measures in $n$-partite systems, $q$-$k$-ME concurrence $(qgeq2,2leq kleq n)$ and $alpha$-$k$-ME concurrence $(0leqalphaleqfrac12,2leq kleq n)$.
Rigorous proofs show that the proposed $k$-nonseparable measures satisfy all the requirements for being an entanglement measure.
arXiv Detail & Related papers (2023-08-31T01:58:47Z) - A spectral least-squares-type method for heavy-tailed corrupted
regression with unknown covariance \& heterogeneous noise [2.019622939313173]
We revisit heavy-tailed corrupted least-squares linear regression assuming to have a corrupted $n$-sized label-feature sample of at most $epsilon n$ arbitrary outliers.
We propose a near-optimal computationally tractable estimator, based on the power method, assuming no knowledge on $(Sigma,Xi) nor the operator norm of $Xi$.
arXiv Detail & Related papers (2022-09-06T23:37:31Z) - Low-Rank Approximation with $1/\epsilon^{1/3}$ Matrix-Vector Products [58.05771390012827]
We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm.
Our main result is an algorithm that uses only $tildeO(k/sqrtepsilon)$ matrix-vector products.
arXiv Detail & Related papers (2022-02-10T16:10:41Z) - Simplest non-additive measures of quantum resources [77.34726150561087]
We study measures that can be described by $cal E(rhootimes N) =E(e;N) ne Ne$.
arXiv Detail & Related papers (2021-06-23T20:27:04Z) - Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample
Complexity [59.34067736545355]
Given an MDP with $S$ states, $A$ actions, the discount factor $gamma in (0,1)$, and an approximation threshold $epsilon > 0$, we provide a model-free algorithm to learn an $epsilon$-optimal policy.
For small enough $epsilon$, we show an improved algorithm with sample complexity.
arXiv Detail & Related papers (2020-06-06T13:34:41Z) - Completing the quantum formalism in a contextually objective framework [0.0]
In standard quantum mechanics, a state vector $| psi rangle$ may belong to infinitely many different orthogonal bases.
In an idealized case, measuring $A$ again and again will give repeatedly the same result, with the same eigenvalue.
The answer is obviously no, since $| psi rangle$ does not specify the full observable $A$ that allowed us to obtain $mu$.
arXiv Detail & Related papers (2020-03-06T10:27:10Z) - A closer look at the approximation capabilities of neural networks [6.09170287691728]
A feedforward neural network with one hidden layer is able to approximate any continuous function $f$ to any given approximation threshold $varepsilon$.
We show that this uniform approximation property still holds even under seemingly strong conditions imposed on the weights.
arXiv Detail & Related papers (2020-02-16T04:58:43Z)
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.