Search:
Match:
4 results
Research Paper#Quantum Information Theory🔬 ResearchAnalyzed: Jan 3, 2026 06:33

No-Cost Nonlocality Certification from Quantum Tomography

Published:Dec 31, 2025 18:59
1 min read
ArXiv

Analysis

This paper presents a novel approach to certify quantum nonlocality using standard tomographic measurements (X, Y, Z) without requiring additional experimental resources. This is significant because it allows for the reinterpretation of existing tomographic data for nonlocality tests, potentially streamlining experiments and analysis. The application to quantum magic witnessing further enhances the paper's impact by connecting fundamental studies with practical applications in quantum computing.
Reference

“Our framework allows any tomographic data - including archival datasets -- to be reinterpreted in terms of fundamental nonlocality tests.”

PermalinkArXiv
Research Paper#Artificial Intelligence, Physics, Discrete Tomography🔬 ResearchAnalyzed: Jan 3, 2026 19:35

Dynamical Ising Machines for Discrete Tomography

Published:Dec 28, 2025 04:57
1 min read
ArXiv

Analysis

This paper introduces a novel application of dynamical Ising machines, specifically the V2 model, to solve discrete tomography problems exactly. Unlike typical Ising machine applications that provide approximate solutions, this approach guarantees convergence to a solution that precisely satisfies the tomographic data with high probability. The key innovation lies in the V2 model's dynamical features, enabling non-local transitions that are crucial for exact solutions. This work highlights the potential of specific dynamical systems for solving complex data processing tasks.
Reference

“The V2 model converges with high probability ($P_{\mathrm{succ}} \approx 1$) to an image precisely satisfying the tomographic data.”

PermalinkArXiv
Research#Tomography🔬 ResearchAnalyzed: Jan 10, 2026 10:12

AI Enhances Single-View Tomographic Reconstruction

Published:Dec 18, 2025 01:19
1 min read
ArXiv

Analysis

This research, published on ArXiv, explores the use of learned primal dual methods for single-view tomographic reconstruction. The application of AI in this field could lead to significant advancements in medical imaging and non-destructive testing.
Reference

“The article is based on research published on ArXiv.”

PermalinkArXiv