Abstract: Cutting quantum circuits is disclosed. Probability distributions for quantum circuit parameters are generated from historical quantum circuit data. When cutting the quantum circuit, the probability distributions are sampled to obtain a set of initial circuit parameters that can function as constraints in the cutting operation.

Abstract: One example method includes receiving a user job that includes a quantum computing workload, evaluating the quantum computing workload, based on the evaluating, solving an orchestration optimization problem by identifying a computing resource for execution of the quantum computing workload, and the computing resource is selected from a defined group of computing resources of a computing infrastructure, and orchestrating the quantum computing workload to the computing resource for execution.

Abstract: Dynamic orchestration of quantum circuit execution. A runtime prediction is performed on a quantum circuit to determine or estimate an amount of resources and execution time that are needed to execute the quantum circuit. The runtime prediction may also estimate the resources, time, and other factors associated with cutting the quantum circuit. When cutting the circuit is beneficial, the quantum subcircuits generated by cutting the quantum circuit are subject to runtime prediction. This information generated by the runtime prediction, along with telemetry data from computing and quantum resources and service level objectives, is used to generate an execution plan for performing or executing the quantum circuit or the quantum subcircuits when the quantum circuit is cut.

Abstract: An apparatus comprises a first processing device, the first processing device comprising a physical hardware controller configured for coupling with a second processing device. The first processing device is configured to identify remote security service instances attached to the second processing device and to initiate, at the first processing device, one or more network emulation modules for the remote security service instances attached to the second processing device that emulate physical network interface devices configured for attachment to the second processing device.

Abstract: Techniques for constructing and otherwise managing knowledge graphs in information processing system environments are disclosed. For example, a method comprises the following steps. The method collects data from a plurality of data sources. The method extracts structured data and unstructured data from the collected data, wherein unstructured data is extracted using an unsupervised machine learning process. The method forms a plurality of sub-graph structures comprising a sub-graph structure for each of the data sources based on at least a portion of the extracted structured data and unstructured data. The method combines the plurality of sub-graph structures to form a combined graph structure representing the collected data from the plurality of data sources. The resulting combined graph structure is a comprehensive knowledge graph.

Abstract: One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Abstract: A computer system for provisioning a data transfer method is disclosed, comprising a processor, and a communications module and a memory coupled to the processor. The memory stores instructions that, when executed by the processor, cause the computer system to: receive a provisioning request for a data transfer method; send, to a server associated with the data transfer method, a request, based on the provisioning request, for assignment of a data transfer identifier; receive, from the server, the data transfer identifier responsive to the request for assignment of the data transfer identifier; obtain a data transfer token corresponding to the data transfer identifier by: sending, to a token service provider, a request for the token comprising the data transfer identifier; receiving, from the token service provider, a data transfer token in response to the request for the token; and, send a reply to the provisioning request comprising the token.

Abstract: Edge security is disclosed. Hosts included in a distributed infrastructure are equipped with data processing units that may be centrally managed using a control plane. Security policies can be selectively distributed to the hosts and localized at the hosts. Reactions to security violations can be initiated at the hosts in substantially real-time. The security policies may take various forms, including rule-based security policies and inference mode-based security policies.

Abstract: A method includes aggregating edge resources of a plurality of edge computing sites of an edge utility system, with the aggregated edge resources including edge resources provided by at least one user for utilization by one or more other users. The method further includes utilizing one or more portions of the aggregated edge resources to execute one or more workloads of the one or more other users, metering the utilization of the one or more portions of the aggregated edge resources in conjunction with the execution of the one or more workloads, and committing information characterizing at least the metered utilization of the aggregated edge resources to a distributed ledger.

Abstract: One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

Abstract: An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Abstract: Techniques are disclosed for chain schedule management for machine learning model-based processing in a computing environment. For example, a method receives a machine learning model-based request and determines a scheduling decision for execution of the machine learning model-based request. Determination of the scheduling decision comprises utilizing a set of one or more scheduling algorithms and comparing results of at least a portion of the set of one or more scheduling algorithms to identify execution environments of a computing environment in which the machine learning model-based request is to be executed. The identified execution environments may then be managed to execute the machine learning model-based request.

Abstract: Techniques described herein relate to a method for managing workflows. The method may include obtaining, from a client, by a registration manager associated with accelerator pools, a request to perform a portion of a workflow using accelerators; identifying a minimum quantity and a maximum quantity of accelerators associated with the request; identifying a storage to store data associated with the portion of the workflow; identifying an accelerator pool of the accelerator pools that includes at least the maximum quantity of accelerators; establishing a connection between the client, the storage, and accelerators of the accelerator pool to perform the portion of the workflow; and initiating the performance of the portion of the workflow using the storage, the client, and the accelerators of the accelerator pool.

Abstract: One example method includes receiving a quadratic unconstrained binary optimization (QUBO) matrix associated with a QUBO problem, determining a classical computing infrastructure on which to execute a simulated first process to solve the QUBO problem, causing execution of the simulated first process to solve the QUBO problem, wherein the execution of the simulated first process is performed on the classical computing infrastructure, and generates a result, and causing execution of an actual, or simulated, second process to solve the QUBO problem, wherein execution of the simulated second process is performed on a quantum computing infrastructure, and the result is used as a starting point for performance of the second process.

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: One method includes receiving a request concerning resolution of a QUBO (quadratic unconstrained binary optimization) problem, randomly generating benchmark QUBOs, obtaining information, including telemetry and solutions, concerning execution of the benchmark QUBOs on each annealer in a group of annealers, and comparing respective performances of each of the annealers. Each of the QUBOs is associated with a respective QUBO matrix, and each QUBO matrix having the same size and density.

Abstract: An orchestration is configured to identify a compilation job and an annealing job of a quantum job. The compilation job and the annealing job may be orchestrated based on estimated costs, resources availability, and service level objectives. The compilation job and the annealing job may be orchestrated in the context of orchestrating multiple quantum jobs. The orchestration engine may orchestrate the compilation and annealing jobs in the same or different systems.

Abstract: Mechanisms of cloning containers to spawn offspring, orchestrate new containers on different execution environments, and enabling intra-container communication while maintaining parent-child relationships are disclosed.

Abstract: Techniques are disclosed for managing digital models in computing environments configured to virtually represent objects in a physical infrastructure. For example, a method comprises detecting an object in a two-dimensional image and identifying an object type of the detected object. The method further comprises selecting, based on the identified object type of the detected object, an algorithm from a plurality of algorithms configured to transform a two-dimensional image into a three-dimensional model.

Abstract: Techniques are disclosed for multi-factor prediction of computing resources for algorithm execution. For example, a method comprises obtaining a set of factors associated with an algorithm configured to transform one or more two-dimensional images into one or more three-dimensional models. The method further comprises computing an estimated computing power value based on the set of factors. The method then comprises scheduling execution of the algorithm on one or more computing resources based on the estimated computing power value.