Edge quantum computing
US-11509599-B1 · Nov 22, 2022 · US
US11901958B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11901958-B2 |
| Application number | US-202217662179-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 5, 2022 |
| Priority date | May 5, 2022 |
| Publication date | Feb 13, 2024 |
| Grant date | Feb 13, 2024 |
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Quantum satellite-based global networks are provided. A system as provided herein includes a processor and a memory that stores first executable instructions that, when executed by the processor, facilitate performance of operations, the operations comprising receiving qubits from a quantum sensor device via a quantum communication channel established between the system and the quantum sensor device; providing quantum input data, derived from the qubits, to a quantum machine learning model; and adjusting a property of a communication network based on an output of the quantum machine learning model, produced in response to the providing of the quantum input data, resulting in an increased performance of a mobile application utilizing resources enabled via the communication network.
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What is claimed is: 1. A system, comprising: a processor; and a memory that stores first executable instructions that, when executed by the processor, facilitate performance of operations, comprising: receiving qubits from a quantum sensor device via a quantum communication channel established between the system and the quantum sensor device; providing quantum input data, derived from the qubits, to a quantum machine learning model; and adjusting a property of a communication network based on an output of the quantum machine learning model, produced in response to the providing of the quantum input data, resulting in an increased performance of a mobile application utilizing resources enabled via the communication network. 2. The system of claim 1 , wherein the adjusting comprises generating recommendation data by performing a prescriptive analytical function on the output of the quantum machine learning model and providing the recommendation data to the communication network via an application programming interface. 3. The system of claim 1 , wherein the quantum communication channel is a first communication channel, and wherein the operations further comprise: receiving binary data via a second communication channel that is distinct from the first communication channel; and generating the quantum input data based on the qubits and the binary data. 4. The system of claim 1 , wherein the quantum sensor device is associated with a satellite traveling in a geocentric orbit. 5. The system of claim 4 , wherein the satellite is positioned at an earth-lunar Lagrange point. 6. The system of claim 1 , wherein the quantum sensor device comprises a device selected from a group of devices comprising a quantum gyroscope, a quantum interferometer, and a quantum accelerometer. 7. The system of claim 1 , wherein the quantum sensor device is an Internet of Things device. 8. The system of claim 1 , wherein the quantum communication channel utilizes a channel feature selected from a group comprising quantum channel encoding and quantum error detection. 9. The system of claim 1 , wherein the quantum input data represents a three-dimensional position of a network device, on which the mobile application is running, with reference to a terrain surface. 10. The system of claim 1 , wherein the communication network is FirstNet, and wherein the mobile application is a public safety mobile application. 11. A method, comprising: receiving, by a system comprising a processor from a quantum sensor device via a photonic communication channel, a data stream comprising qubits; providing, by the system to a quantum machine learning model, quantum input data based on the qubits; and facilitating, by the system, an adjustment of a property of a communication network based on an output of the quantum machine learning model, produced in response to the providing of the quantum input data, resulting in an increase in a performance metric associated with a mobile application enabled via the communication network. 12. The method of claim 11 , wherein the facilitating of the adjustment comprises: performing a prescriptive analytical function on the output of the quantum machine learning model, resulting in prescriptive analytics data; and providing the prescriptive analytics data to the communication network via an application programming interface. 13. The method of claim 11 , wherein the data stream is a first data stream, and wherein the method further comprises: receiving, by the system via a non-photonic communication channel, a second data stream comprising binary data; and generating, by the system, the quantum input data based on the qubits and the binary data. 14. The method of claim 11 , wherein the quantum sensor device is associated with a satellite traveling in a geocentric orbit. 15. The method of claim 11 , wherein the quantum sensor device is an Internet of Things device. 16. The method of claim 11 , wherein the quantum input data represents a three-dimensional position of a mobile device, running the mobile application via the communication network, with reference to a terrain surface. 17. A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising: receiving, from a quantum sensor via a quantum communication channel, quantum sensor measurement data; deriving model input data from the quantum sensor measurement data; providing the model input data to a quantum machine learning model; and modifying a property of a communication network based on an output of the quantum machine learning model, produced in response to the model input data, wherein the modifying of the property of the communication network results in increased performance of a mobile application utilizing resources associated with the communication network. 18. The non-transitory machine-readable medium of claim 17 , wherein the modifying comprises: generating recommendation data by performing a prescriptive analytical function on the output of the quantum machine learning model; and conveying the recommendation data to network equipment of the communication network via an application programming interface. 19. The non-transitory machine-readable medium of claim 17 , wherein the operations further comprise: receiving, via a non-photonic communication channel, supplemental input data that is distinct from the quantum sensor measurement data, wherein the deriving of the model input data, comprises deriving the model input data based on the quantum sensor measurement data and the supplemental input data. 20. The non-transitory machine-readable medium of claim 17 , wherein the quantum sensor measurement data relates to a three-dimensional position of a mobile device, on which the mobile application is executing, with reference to a terrain surface.
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