Abstract: A method for enabling user control of a display screen. The example method includes tracking, by a camera, a position of a pupil of an eye of a human user who is positioned to be able to view multiple display screens, determining a location of the pupil within the eye, determining, as among the display screens, and based on the location of the pupil, which of the display screens is being looked at by the user, and enabling control, by the user, of the display screen that the user is looking at.

Abstract: One example method includes evaluating a function invoked by a request that is received at a local classical computing execution environment, and the request also implies performance of a quantum computing function in a quantum computing execution environment, based on an outcome of the evaluating, determining whether or not the function should be run in the local classical computing execution environment, or whether the function should be run in a separate classical computing execution environment, and when the determining indicates that the function should be run in the separate classical computing execution environment, forwarding the request to the separate classical computing environment for execution of the function. The local classical computing execution environment, the separate classical computing execution environment, and the quantum computing execution environment, are respective first, second, and third, tiers of a hybrid computing execution environment.

Abstract: One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Abstract: One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Abstract: Techniques are disclosed for management of edge resources. For example, a method comprises receiving energy consumption data corresponding to operation of a plurality of edge devices from a plurality of edge service providers. In the method, a plurality of energy efficiency scores are computed based, at least in part, on the energy consumption data. The energy efficiency scores correspond to operation of one or more of the edge devices associated with respective ones of the edge service providers. The method further comprises receiving one or more energy consumption parameters from at least one user device for the operation of the one or more edge devices, and identifying based, at least in part, on the energy efficiency scores, a subset of the edge devices corresponding to the energy consumption parameters. Data corresponding to the subset of the edge devices is transmitted to the at least one user device.

Abstract: One example method includes obtaining information about a first pre-defined implementation of a QUBO (quadratic unconstrained binary optimization) problem configured for execution on a gate-based device, obtaining information about a second pre-defined implementation of the QUBO problem configured for execution on an annealing device, receiving information about a QUBO job that is to be executed, identifying first hardware and second hardware that are available to execute the QUBO job, and the first hardware is different from the second hardware, using the information about the first and second pre-defined implementations of the QUBO problem to generate respective predictions concerning performance of the QUBO job on the first hardware and the second hardware, comparing the predictions, and based on the comparing, selecting one of the first hardware and the second hardware for execution of the QUBO job.

Abstract: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: Techniques are disclosed for management of edge devices. For example, a method comprises coordinating operation of a plurality of edge devices in a system to process a plurality of workloads. The coordinating of the operation of the plurality of edge devices in the system comprises coordinating one or more times for changing a state of at least a subset of the plurality of edge devices from a first state corresponding to a first level of activity to a second state corresponding to a second level of activity. By way of further example, the coordinating of the operation of the plurality of edge devices in the system may further comprise coordinating one or more times for the processing of the plurality of workloads by at least the subset of the plurality of edge devices in the system.

Abstract: Quantum job prioritization is disclosed. Quantum jobs may be stored as placeholders in a job queue associated with a quantum processing unit. The quantum jobs are prioritized to improve the usage of the quantum processing unit. Prioritizing quantum jobs allows the quantum processing unit to execute quantum jobs in different orders rather than on an application basis. This allows grace periods to be used for executing quantum jobs.

Abstract: Distributing quantum jobs are disclosed. When a quantum processing unit is underutilized or when wait times are long, quantum jobs may be distributed from the job queue of one vendor to another vendor. This improves utilization and reduces wait times.

Abstract: One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Abstract: One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Abstract: Global optimization of quantum jobs in a multi-cloud or multi-edge environment is disclosed. The quantum jobs of multiple vendors are consolidated in a telemetry plane. The quantum jobs are evaluated based on user intents, quantum job characteristics, and quantum processing unit characteristics. The quantum jobs are then assigned to the quantum systems of the vendors based on the evaluation.

Abstract: One example method includes evaluating a function invoked by a request that is received at a local classical computing execution environment, and the request also implies performance of a quantum computing function in a quantum computing execution environment, based on an outcome of the evaluating, determining whether or not the function should be run in the local classical computing execution environment, or whether the function should be run in a separate classical computing execution environment, and when the determining indicates that the function should be run in the separate classical computing execution environment, forwarding the request to the separate classical computing environment for execution of the function. The local classical computing execution environment, the separate classical computing execution environment, and the quantum computing execution environment, are respective first, second, and third, tiers of a hybrid computing execution environment.

Abstract: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: One example method includes receiving job configuration information from a user with a quantum computing job to be performed, receiving quantum computing information from a quantum computing service vendor, generating, based on the quantum computing information, a vendor score for the quantum computing service vendor, and transmitting the vendor score to the user. The quantum computing information received from the quantum computing service vendor may include information about an accuracy of results produced by execution of a quantum circuit or other quantum hardware operated by the quantum computing service vendor.

Abstract: Techniques are disclosed for management of edge devices. For example, a method comprises coordinating operation of a plurality of edge devices in a system to process a plurality of workloads. The coordinating of the operation of the plurality of edge devices in the system comprises coordinating one or more times for changing a state of at least a subset of the plurality of edge devices from a first state corresponding to a first level of activity to a second state corresponding to a second level of activity. By way of further example, the coordinating of the operation of the plurality of edge devices in the system may further comprise coordinating one or more times for the processing of the plurality of workloads by at least the subset of the plurality of edge devices in the system.

Abstract: Techniques are disclosed for management of edge resources. For example, a method comprises receiving energy consumption data corresponding to operation of a plurality of edge devices from a plurality of edge service providers. In the method, a plurality of energy efficiency scores are computed based, at least in part, on the energy consumption data. The energy efficiency scores correspond to operation of one or more of the edge devices associated with respective ones of the edge service providers. The method further comprises receiving one or more energy consumption parameters from at least one user device for the operation of the one or more edge devices, and identifying based, at least in part, on the energy efficiency scores, a subset of the edge devices corresponding to the energy consumption parameters. Data corresponding to the subset of the edge devices is transmitted to the at least one user device.

Abstract: A system includes a host processor, a volatile memory device coupled to the host processor, and at least a first persistent memory device coupled to the host processor. The host processor is configured to execute one or more applications. The volatile memory device and the first persistent memory device are in respective distinct fault domains of the system, and at least one of a plurality of data objects generated by a given one of the applications is accessible from multiple distinct storage locations in respective ones of the distinct fault domains. For example, the host processor and the volatile memory device may be in a first one of the distinct fault domains and the first persistent memory device may be in a second one of the distinct fault domains. The data object remains accessible in one of the fault domains responsive to a failure in another of the fault domains.

Abstract: A system includes a host processor, a volatile memory device coupled to the host processor, and at least a first persistent memory device coupled to the host processor. The host processor is configured to execute one or more applications. The volatile memory device and the first persistent memory device are in respective distinct fault domains of the system, and at least one of a plurality of data objects generated by a given one of the applications is accessible from multiple distinct storage locations in respective ones of the distinct fault domains. For example, the host processor and the volatile memory device may be in a first one of the distinct fault domains and the first persistent memory device may be in a second one of the distinct fault domains. The data object remains accessible in one of the fault domains responsive to a failure in another of the fault domains.