High-fidelity entanglement of two spectrally-distinct solid state emitters achieves a concurrence of 0.95 using narrowband photon inputs. The method uses readily available entangled photon pairs to entangle emitters with large transition frequency separations and linewidth variations, removing the need for identical emitters.
Key points
Concurrence C = 0.95 for typical quantum dots.
Entangles emitters with large frequency and linewidth differences using entangled photon inputs.
Sources
symd_atlas - B1-001 launches web atlas of 2D wallpaper group simulations
symd_atlas - B1-001 provides an interactive WebGPU atlas of dynamic simulations for all 17 2D wallpaper groups. The atlas was ported from a C simulation engine using Opus-5.5 and is now viewable in a browser. It replaces the previous workflow that required running a C engine, a Python conversion script, and a special viewer.
Key points
WebGPU implementation enables live, browser-based viewing of the 2D wallpaper group simulations.
The original C engine and Python pipeline have been replaced by a single web application.
Sources
- Original post
- symdatlas - B1-001 - Linked page
Molecule Labs partner licensing read-only access
Molecule Labs lets partners have timeboxed, read-only access to confidential work, with files encrypted before leaving the device. No security trade-off is required for collaboration.
Key points
Timeboxed, read-only partner access
Files encrypted before leaving the device
Sources
Can Entanglement-Induced Feature-Map Designs Improve Projected Quantum Kernels?
Can Entanglement-Induced Feature-Map Designs Improve Projected Quantum Kernels? introduces quantum feature maps that use two-qubit entanglement to capture multivariate correlations in IoT data. The paper isolates native inductive bias by comparing untuned and tuned projected quantum kernels against an RBF baseline. Results indicate that the proposed entanglement topologies exhibit a significantly stronger native inductive bias than standard designs.
Key points
Untuned and tuned projected quantum kernels are benchmarked against an RBF baseline to expose native inductive bias.
Entanglement-based feature maps show a significantly stronger native inductive bias than conventional designs.
