Abstract: A device may include a processor configured to select a quantum key distribution transmission; identify an optical fiber path via which the quantum key distribution transmission is to be performed; determine one or more values for at least one transmission parameter for the identified optical fiber path; and select a pulse script for the optical fiber path based on the determined one or more values for the at least one transmission parameter. The processor may be further configured to perform the quantum key distribution transmission via the identified optical fiber path using the selected pulse script.

Abstract: Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features.

Abstract: Embodiments described herein provide for the granular network-based detection of UE location in a RAN that includes one or more mobile base stations using quantum computing. Mobile base stations may be, for example, affixed on vehicles (e.g., cars, trucks, drones, etc.), may be implemented by other UEs, and/or may otherwise be non-stationary. In contrast, fixed base stations may be mounted to towers, buildings, or other types of permanent or semi-permanent installations. Quantum computing techniques, as described herein, may aid in the precise determination of UE location using triangulation techniques and/or other network-based location techniques. Further, in RANs that include mobile base stations, the locations of both the UE and a reference point may change relatively rapidly. The use of quantum computing, as described herein, may aid in the fast and precise determination of UE location in situations where mobile base stations and/or UEs are moving rapidly.

Abstract: A device may include a processor configured to select a quantum key distribution transmission; identify an optical fiber path via which the quantum key distribution transmission is to be performed; determine one or more values for at least one transmission parameter for the identified optical fiber path; and select a pulse script for the optical fiber path based on the determined one or more values for the at least one transmission parameter. The processor may be further configured to perform the quantum key distribution transmission via the identified optical fiber path using the selected pulse script.

Abstract: Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features.

Abstract: A device may include a processor configured to select a quantum key distribution transmission; identify an optical fiber path via which the quantum key distribution transmission is to be performed; determine one or more values for at least one transmission parameter for the identified optical fiber path; and select a pulse script for the optical fiber path based on the determined one or more values for the at least one transmission parameter. The processor may be further configured to perform the quantum key distribution transmission via the identified optical fiber path using the selected pulse script.

Abstract: A cognitive database system matches a particular data element, that is specified as part of received user input, to a first set of data elements in a set of data models based on semantic commonality, and matches the particular data element to a second set of data elements in the set of data models based on visual commonality between graphical representations of the particular data element and the second set of data elements. The cognitive database may determine interdependency between the particular data element and a particular subset of the first and second sets of data elements based on a measure of semantic commonality and/or visual commonality, and may generate a response to the user input with values from the particular subset of data elements that are updated directly from the interdependency that is determined upon receiving the user input.

Abstract: A device may include a processor configured to select a quantum key distribution transmission; identify an optical fiber path via which the quantum key distribution transmission is to be performed; determine one or more values for at least one transmission parameter for the identified optical fiber path; and select a pulse script for the optical fiber path based on the determined one or more values for the at least one transmission parameter. The processor may be further configured to perform the quantum key distribution transmission via the identified optical fiber path using the selected pulse script.

Abstract: Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features.

Abstract: Embodiments described herein provide for the granular network-based detection of UE location in a RAN that includes one or more mobile base stations using quantum computing. Mobile base stations may be, for example, affixed on vehicles (e.g., cars, trucks, drones, etc.), may be implemented by other UEs, and/or may otherwise be non-stationary. In contrast, fixed base stations may be mounted to towers, buildings, or other types of permanent or semi-permanent installations. Quantum computing techniques, as described herein, may aid in the precise determination of UE location using triangulation techniques and/or other network-based location techniques. Further, in RANs that include mobile base stations, the locations of both the UE and a reference point may change relatively rapidly. The use of quantum computing, as described herein, may aid in the fast and precise determination of UE location in situations where mobile base stations and/or UEs are moving rapidly.

Abstract: Embodiments described herein provide for the granular network-based detection of UE location in a RAN that includes one or more mobile base stations using quantum computing. Mobile base stations may be, for example, affixed on vehicles (e.g., cars, trucks, drones, etc.), may be implemented by other UEs, and/or may otherwise be non-stationary. In contrast, fixed base stations may be mounted to towers, buildings, or other types of permanent or semi-permanent installations. Quantum computing techniques, as described herein, may aid in the precise determination of UE location using triangulation techniques and/or other network-based location techniques. Further, in RANs that include mobile base stations, the locations of both the UE and a reference point may change relatively rapidly. The use of quantum computing, as described herein, may aid in the fast and precise determination of UE location in situations where mobile base stations and/or UEs are moving rapidly.

Abstract: A cognitive database system matches a particular data element, that is specified as part of received user input, to a first set of data elements in a set of data models based on semantic commonality, and matches the particular data element to a second set of data elements in the set of data models based on visual commonality between graphical representations of the particular data element and the second set of data elements. The cognitive database may determine interdependency between the particular data element and a particular subset of the first and second sets of data elements based on a measure of semantic commonality and/or visual commonality, and may generate a response to the user input with values from the particular subset of data elements that are updated directly from the interdependency that is determined upon receiving the user input.

Abstract: Systems and methods provide a demand forecasting and network optimization for telecommunications services in a network. The systems and methods use classical and quantum computing devices. The computing devices evaluate data types using statistical symmetry recognition and operate between classical and quantum environments. Computing devices receive deposited data, batch data, and streamed data that relates to telecommunications services and segregate the data into spatial and temporal factors. The computing devices receive an analytic request for a forecast of the telecommunications services and conduct a multi-class plural-factored elastic cluster (MPEC) analysis for the telecommunications services using the segregated data. The MPEC analysis includes generating vectors comprised of slopes from plural coefficients to determine demand elasticity from plural features.

Abstract: A system described herein may provide a technique for the use of artificial intelligence/machine learning (“AI/ML”) techniques to model User Equipment (“UE”) usage information based on battery dissipation and/or other battery performance or characteristic information. A given model (or set of models) may be used to determine UE usage information, such as types of applications or services (e.g., voice calls, content streaming, web browsing, etc.) being used by UEs, amount of traffic sent and/or received by UEs, radio access network (“RAN”) connection or disconnection activity, and/or other types of UE usage information. Network parameters, such as beam configuration parameters, may be modified based on UE usage information determined based on such models.