US 12,450,677 B2
System and method for quantum digital twinning and public safety management
Mark Stockert, San Antonio, TX (US); Thomas J. Routt, Sequim, WA (US); and Jerry Robinson, Middletown, NJ (US)
Assigned to AT&T Intellectual Property I, L.P., Atlanta, GA (US); and AT&T Mobility II LLC, Atlanta, GA (US)
Filed by AT&T Intellectual Property I, L.P., Atlanta, GA (US); and AT&T Mobility II LLC, Atlanta, GA (US)
Filed on Oct. 11, 2022, as Appl. No. 17/963,657.
Prior Publication US 2024/0119550 A1, Apr. 11, 2024
Int. Cl. G06Q 50/26 (2024.01); G06N 5/045 (2023.01); G06N 10/20 (2022.01)
CPC G06Q 50/265 (2013.01) [G06N 5/045 (2013.01); G06N 10/20 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A device, comprising:
a quantum processing system including a processor; and
a memory that stores executable instructions that, when executed by the quantum processing system, facilitate performance of operations, the operations comprising:
acquiring, in real time, multi-modal sensor data including quantum illumination data, quantum holographic sensor data, and conventional information from distributed sources;
processing the multi-modal sensor data using quantum entanglement-based imaging to generate three-dimensional images with a spatial resolution exceeding a Rayleigh limit, thereby improving an accuracy of object detection and classification in low-visibility or high-noise environments;
storing the three-dimensional images and classified objects as a graph in a quantum graph database, wherein the quantum graph database enables faster retrieval and analysis of spatial relationships between objects compared to classical databases;
creating a quantum digital twinning model of a public safety event based on the multi-modal sensor data and the quantum graph database;
generating a map view of an area, wherein the map view shows images determined by the quantum digital twinning model corresponding to the public safety event;
providing recommendations for actions to mitigate the public safety event, wherein the recommendations are determined from the quantum digital twinning model; and
generating and providing an analysis explaining how the recommendations were determined by the quantum processing system, wherein the analysis includes a visualization of causal relationships between detected objects and recommended actions generated using quantum-classical federated reinforcement learning, thereby improving user trust and decision-making speed.