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 root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Abstract: Methods, systems, and apparatus for root cause analysis in a communication network. In one aspect, a method includes providing a quantum computer with data representing a topology of the communication network, the topology comprising a graph of vertices representing network devices and edges representing connections between network devices; receiving, from the quantum computer, data representing a first subset of network devices, wherein the first subset comprises a dominating set of vertices or a vertex cover for the graph; monitoring network devices in the first subset to generate alarm data representing triggered network device alarms; providing the alarm data to a quantum computer; receiving, from the quantum computer, data representing a second subset of network devices, wherein the second subset comprises a set cover for the alarm data and the network devices in the second subset comprise diagnosed sources of failures in the communication network.

Abstract: Methods, systems, and apparatus for generating intensity modulated radiation therapy treatment plans.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Implementations directed to providing a computer-implemented system for performing an action with a robot comprising receiving command information indicating a command related to performance of an action with a robot, identifying state information for a plurality of active routines that are actively running for the robot, the state information indicating a state for each of the active routines, determining contextual information for the command based on the accessed state information for the plurality of active routines, selecting one of the active routines as a handling routine to service the command based on the contextual information, determining an output module of the robot to perform the action based on the state of the handling routine and the contextual information, and executing one or more instructions to perform the action with the output module.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.

Abstract: Methods, systems, and apparatus for improving exchange systems. In one aspect, a method includes receiving data representing an exchange problem; determining, from the received data, an integer programming formulation of the exchange problem; mapping the integer programming formulation of the exchange problem to a quadratic unconstrained binary optimization (QUBO) formulation of the exchange problem; obtaining data representing a solution to the exchange problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the exchange problem.

Abstract: Methods, systems, and apparatus for improving exchange systems. In one aspect, a method includes receiving data representing an exchange problem; determining, from the received data, an integer programming formulation of the exchange problem; mapping the integer programming formulation of the exchange problem to a quadratic unconstrained binary optimization (QUBO) formulation of the exchange problem; obtaining data representing a solution to the exchange problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the exchange problem.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Methods and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

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, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.

Abstract: Implementations include deploying portions of a model used in a cognitive computing process for execution on respective devices in a distributed system, and include processing a device list to provide a benchmark list that includes an inventory of devices across systems that provide data for execution of the model used in the cognitive computing process, each device being a candidate for executing at least a portion of the model, the benchmark list including the devices, and, for each device, accuracies of the device with respect to the at least a portion of the model, providing a deployment plan based on the device and benchmark lists, the deployment plan specifying tasks to be executed by each device, at least two devices included in respective systems, at least one device providing data that is to be processed based on the at least a portion of the model, and executing the deployment plan.

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: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for enhancing digital content provided from devices, and actions of providing, by a sensor of a device, primary digital content, providing, by the device, secondary digital content, the secondary digital content indicating a context, within which the primary digital content was generated, generating, by the device, a data package including the primary digital content and the secondary digital content, and transmitting, by the device, the data package to a back-end system over a network.

Abstract: Implementations directed to providing a computer-implemented system for performing an action with a robot comprising receiving command information indicating a command related to performance of an action with a robot, identifying state information for a plurality of active routines that are actively running for the robot, the state information indicating a state for each of the active routines, determining contextual information for the command based on the accessed state information for the plurality of active routines, selecting one of the active routines as a handling routine to service the command based on the contextual information, determining an output module of the robot to perform the action based on the state of the handling routine and the contextual information, and executing one or more instructions to perform the action with the output module.

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 and systems for a quantum computing approach to solving challenging, e.g., NP-complete, problems in transportation. One of the methods includes (a) ingesting transportation-related data into a graph structure, the transportation-related data being associated with a transportation system; (b) identifying a transportation metric associated with the transportation system; (c) identifying at least one attribute associated with the transportation-related data, where the transportation metric is based at least in part on the attribute; (d) using a quantum computer to derive an operational parameter for the attribute that improves the transportation metric; and (e) applying the operational parameter to the operation of the transportation system.

Abstract: Implementations include deploying portions of a model used in a cognitive computing process for execution on respective devices in a distributed system, and include processing a device list to provide a benchmark list that includes an inventory of devices across systems that provide data for execution of the model used in the cognitive computing process, each device being a candidate for executing at least a portion of the model, the benchmark list including the devices, and, for each device, accuracies of the device with respect to the at least a portion of the model, providing a deployment plan based on the device and benchmark lists, the deployment plan specifying tasks to be executed by each device, at least two devices included in respective systems, at least one device providing data that is to be processed based on the at least a portion of the model, and executing the deployment plan.