Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Methods are provided in which a computing device obtains telemetry data associated with a network technology used in an enterprise network. The network technology is deployed using one or more assets of the enterprise. The methods further include the computing device determining, for each deployment of the network technology, a progression along an adoption lifecycle of the network technology, based on the telemetry data, generating, based on the progression of each deployment of the network technology, a first action set and an alternative second action set, different from the first action set, for further progression along the adoption lifecycle and providing the first action set and the alternative second action set for the further progression of the network technology along the adoption lifecycle.

Abstract: Methods are provided in which a computing device obtains telemetry data associated with a network technology used in an enterprise network. The network technology is deployed using one or more assets of the enterprise. The methods further include the computing device determining, for each deployment of the network technology, a progression along an adoption lifecycle of the network technology, based on the telemetry data, generating, based on the progression of each deployment of the network technology, a first action set and an alternative second action set, different from the first action set, for further progression along the adoption lifecycle and providing the first action set and the alternative second action set for the further progression of the network technology along the adoption lifecycle.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Abstract: Computer systems and methods for improving a computer's ability to interface with a user to configure one or more internetworking services provided by a first group of computers for a second group of computers using a pictorial graph with nodes that represent logical network objects.

Abstract: In one embodiment, a system comprising camera(s) adapted to capture first image(s) including display surface(s), touchscreen(s) adapted to detect user input, and processor(s) adapted to display, simultaneously on the touchscreen(s), an augmented reality user interface including the first image(s), and other item(s) not captured by the camera(s), the other item(s) including at least one of: control item(s), or content item(s), interpret the user input detected by the touchscreen(s) to include selection of a first item in the first image(s), and a second content item, determine that the first item is a first display surface of the display surface(s) in the first image(s), and cause the second content item to be moved to, or duplicated to, or moved from, or duplicated from, the first display surface.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Abstract: Disclosed is a graphical user interface for a messaging and orchestrating platform. The graphical user interface can include a first region of the graphical user interface that represents a topology of a network environment including one or more devices and one or more applications and a second region of the graphical user interface that represents a messaging platform for communicating with one or more members and the one or more devices and the one or more applications.

Abstract: Methods, systems, and apparatus for training a machine learning model to route received computational tasks in a system including at least one quantum computing resource. In one aspect, a method includes obtaining a first set of data, the first set of data comprising data representing multiple computational tasks previously performed by the system; obtaining input data for the multiple computational tasks previously performed by the system, comprising data representing a type of computing resource the task was routed to; obtaining a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks; and training the machine learning model to route received data representing a computational task to be performed using the (i) first set of data, (ii) input data, and (iii) second set of data.

Abstract: In one embodiment, a system includes: a processor, a display screen, and a user interface (UI) application to be executed by the processor and operative: to present policy attributes in a UI with interactive tradeoff analysis on the display screen, where the policy attributes are associated with a network workflow structure, and the interactive tradeoff analysis includes constraint-based optimization of a workflow response, and to dynamically apply said policy attributes to instantiation and deployment of networked resources associated with the network workflow structure.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Abstract: Computer systems and methods for improving a computer's ability to interface with a user to configure one or more internetworking services provided by a first group of computers for a second group of computers using a pictorial graph with nodes that represent logical network objects.

Abstract: Techniques for sorting and filtering a list in a space-constrained viewing area are described. A filter widget and a list of items are displayed in a view of an electronic display screen. In response to user input selecting the filter widget, and without changing to a different view, a filter category overlay is displayed in the view to provide various selectable filter categories. In response to selection of a filter category, a visual depiction of the selected filter category is displayed in the view. The list of items is filtered according to the selected filter category and displayed in the view without changing to any other view. Subsequently added visual depictions are linearly stacked in the view. Further, a filter modification overlay, which provides selectable filtering attributes, is displayed upon selection of a visual depiction.

Abstract: Techniques for sorting and filtering a list in a space-constrained viewing area are described. A filter widget and a list of items are displayed in a view of an electronic display screen. In response to user input selecting the filter widget, and without changing to a different view, a filter category overlay is displayed in the view to provide various selectable filter categories. In response to selection of a filter category, a visual depiction of the selected filter category is displayed in the view. The list of items is filtered according to the selected filter category and displayed in the view without changing to any other view. Subsequently added visual depictions are linearly stacked in the view. Further, a filter modification overlay, which provides selectable filtering attributes, is displayed upon selection of a visual depiction.

Abstract: Techniques for sorting and filtering a list in a space-constrained viewing area are described. A filter widget and a list of items are displayed in a view of an electronic display screen. In response to user input selecting the filter widget, and without changing to a different view, a filter category overlay is displayed in the view to provide various selectable filter categories. In response to selection of a filter category, a visual depiction of the selected filter category is displayed in the view. The list of items is filtered according to the selected filter category and displayed in the view without changing to any other view. Subsequently added visual depictions are linearly stacked in the view. Further, a filter modification overlay, which provides selectable filtering attributes, is displayed upon selection of a visual depiction.

Abstract: Techniques for sorting and filtering a list in a space-constrained viewing area are described. A filter widget and a list of items are displayed in a view of an electronic display screen. In response to user input selecting the filter widget, and without changing to a different view, a filter category overlay is displayed in the view to provide various selectable filter categories. In response to selection of a filter category, a visual depiction of the selected filter category is displayed in the view. The list of items is filtered according to the selected filter category and displayed in the view without changing to any other view. Subsequently added visual depictions are linearly stacked in the view. Further, a filter modification overlay, which provides selectable filtering attributes, is displayed upon selection of a visual depiction.

Abstract: Techniques for sorting and filtering a list in a space-constrained viewing area are described. A filter widget and a list of items are displayed in a view of an electronic display screen. In response to user input selecting the filter widget, and without changing to a different view, a filter category overlay is displayed in the view to provide various selectable filter categories. In response to selection of a filter category, a visual depiction of the selected filter category is displayed in the view. The list of items is filtered according to the selected filter category and displayed in the view without changing to any other view. Subsequently added visual depictions are linearly stacked in the view. Further, a filter modification overlay, which provides selectable filtering attributes, is displayed upon selection of a visual depiction.