Kernel State Reconstruction
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Related Articles from SNS
Knowledge-Informed Kernel State Reconstruction from Heterogeneous Partial Observations
arXiv:2601.22328v2 Announce Type: replace Abstract: Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, noisy, and heterogeneous, providing fragmented views of latent dynamical states. We introduce MAAT (Model Aware Approximation of Trajectories), a framework for knowledge-informed Kernel State Reconstruction in partially observed dynamical systems.
Continuous-Variable Quantum State Tomography Enabled by Quantum Mirrors
Announce Type: cross Abstract: In quantum technologies, continuous-variable systems offer advantages over their discrete counterparts. However, continuous-variable tomography suffers from exponentially growing sample complexity. We propose protocols using quantum mirrors to transfer the complete information of incident photonic states onto a control atomic system.
Quantum feature-map learning with reduced resource overhead
Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction.
Boundary-compatible interacting approximations of quasilinear PDEs on bounded domains
arXiv:2606.04049v1 Announce Type: new Abstract: We develop a general operator-theoretic route that turns Kato-type quasilinear evolution systems on a Banach scale $(Z,X)$ into finite-dimensional interacting approximations. The construction proceeds in two steps. First, one introduces a regularized family $(A_\varepsilon,f_\varepsilon)$ indexed by a scale parameter $\varepsilon>0$, for which the drift $A_\varepsilon[t,z]z+f_\varepsilon[t,z]$ takes values in an output space $Y$ suitable for...
Guided progressive reconstructive imaging: a new quantization-based framework for low-dose, high-throughput and real-time analytical ptychography
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Efficient and accurate neural-field reconstruction using resistive memory
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Ahoy, DECmate II the little PDP-8 that could
Now, that's a lot of word processing. But under the hood it's still at least PDP-8 adjacent, even considering its oddities and incompatibilities, and you can make it do many of the things a full-size Eight can. We'll take this basic unit, convert the floppy drives to solid state, tap the video output, and put it through its paces.