System and method for quantum digital twinning and public safety management

Patent metadata

Field	Value
Publication number	US-12450677-B2
Application number	US-202217963657-A
Country	US
Kind code	B2
Filing date	Oct 11, 2022
Priority date	Oct 11, 2022
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

Aspects of the subject disclosure may include, for example a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: creating a quantum digital twinning model for a public safety event; generating a map view of an area of the public safety event, wherein the map view shows images determined by the quantum digital twinning model; providing recommendations for actions to mitigate the public safety event, wherein the recommendations are determined from the quantum digital twinning model; and providing explainability of the recommendations determined. Other embodiments are disclosed.

First claim

What is claimed is:

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.

2. The device of claim 1 , wherein the operations further comprise changing the map view from a higher level of abstraction to a more detailed level.

3. The device of claim 2 , wherein the more detailed level provides a much higher granularity.

4. The device of claim 1 , wherein the operations further comprise providing selectable layers in the map view.

5. The device of claim 1 , wherein the operations further comprise combining localized quantum digital twinning models into a composite global model of a wider area of the public safety event.

6. The device of claim 1 , wherein the operations further comprise providing a future prediction of a time-lapsed progression of the public safety event from the quantum digital twinning model.

7. The device of claim 1 , wherein the analysis is tailored for a user of the device.

8. The device of claim 7 , wherein the analysis is simplified by providing a more generalized description of the recommendations that were determined.

9. The device of claim 1 , wherein the conventional information comprises building information, video, acoustic, motion, classical images from mobile devices, drones, Internet of Things (IoT) sensors, map data, building floor plan maps, satellite sensor data, environmental sensor data, geo-tagged location data, social network data feeds/crowd sourced data, or a combination thereof, and wherein the quantum digital twinning model comprises the three-dimensional images with the classified objects.

10. The device of claim 1 , wherein the quantum processing system comprises quantum federated reinforced learning.

11. The device of claim 10 , wherein the quantum processing system comprises a plurality of quantum processors operating in a distributed computing environment.

12. A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a quantum processing system including a processor, 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;
recommending actions to mitigate damage of 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.

13. The non-transitory, machine-readable medium of claim 12 , wherein the operations further comprise changing the map view from a higher level of abstraction to a more detailed level.

14. The non-transitory, machine-readable medium of claim 13 , wherein the more detailed level provides a higher granularity.

15. The non-transitory, machine-readable medium of claim 12 , wherein the operations further comprise providing selectable layers in the map view, wherein the selectable layers comprise electrical distribution system graphs, water distribution system graphs, street, road, and highway system graphs, wireless, cable and wired/optical network system graphs, traffic management system graphs, vehicle system graphs, or a combination thereof.

16. The non-transitory, machine-readable medium of claim 12 , wherein the operations further comprise combining localized quantum digital twinning models into a composite global model of a wider area of the public safety event.

17. The non-transitory, machine-readable medium of claim 12 , wherein the operations further comprise providing a future prediction of a time-lapsed progression of the public safety event from the quantum digital twinning model.

18. The non-transitory, machine-readable medium of claim 12 , wherein the analysis is simplified and tailored to a user.

19. The device of claim 1 , wherein the quantum processing system comprises a plurality of quantum processors, classical processors, or a combination thereof operating in a distributed computing environment.

20. A method, 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 classic

Assignees

• At & T Ip I Lp
• At & T Mobility Ii Llc

Inventors

• Stockert Mark
• Routt Thomas J
• Robinson Jerry

Classifications

• G06N10/20

  Models of quantum computing, e.g. quantum circuits or universal quantum computers · CPC title

• G06N5/045

  Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence · CPC title

• G06Q50/265 Primary

  Personal security, identity or safety · CPC title