Abstract: A fabric care composition is provided including water; a modified carbohydrate polymer having a weight average molecular weight of <500,000 Daltons and a Kjeldahl nitrogen content corrected for ash and volatiles, TKN, of ?0.5 wt %; and a cleaning surfactant; wherein the modified carbohydrate polymer is a carbohydrate polymer functionalized with quaternary ammonium moieties; wherein the quaternary ammonium moieties on the modified carbohydrate polymer include: trimethyl ammonium moieties having formula (I) and dimethyl(alkyl) ammonium moieties having formula (II) wherein each R is independently selected from a C8-22 alkyl group.

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 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: 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: 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: Techniques are disclosed for decentralized data flow valuation and deployment in a highly distributed system. For example, a method comprises the following steps performed in a highly distributed system comprising a plurality of gateway nodes, wherein each of the plurality of gateway nodes is operatively coupled to one or more sensors that provide sensor data to the corresponding gateway node. A data flow is obtained at a given gateway node from a decentralized data storage system upon which a plurality of data flows is stored. The data flow is executed at the given gateway node, wherein the data flow is executed on sensor data obtained by the given gateway node from the corresponding one or more sensors. A data flow valuation operation is performed at the given gateway node based on the executed data flow to generate a data flow valuation result.

Abstract: One example method includes receiving a query that recites a particular question for which a user who originated the query needs an answer, parsing the query to identify the question, identifying information that is responsive to the question, presenting the information to the user in a user-selectable form, and receiving, from the user, a selection of the information. In some cases, the information presented to the user may include one or more datasets, or one or more pipelines.

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 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: One example method includes identifying an accelerator service instance associated with a workload, calling a service broker associated with the accelerator service instance to obtain information needed to use the accelerator service instance, receiving an accelerator call, and accelerator job information concerning an accelerator job, from the workload, in response to the accelerator call, spinning up a new process dedicated to the accelerator job, as part of the new process, running the accelerator job using either the accelerator service instance, or a locally available accelerator, and returning data, generated by running the accelerator job, to the workload.

Abstract: One example method includes receiving, at a node of a data confidence fabric (DCF), a DCF backbone, installing the DCF backbone at the node, receiving a config file at the node, and the config file includes configuration information concerning the node, and receiving and installing a trust insertion component specified in the configuration information, where operation of the trust insertion component is enabled by the DCF backbone, and the trust insertion component is operable to associate trust metadata with data received by the node.

Abstract: One example method includes detecting a threat in a data confidence fabric, assigning a data confidence score to data implicated by the threat, generating trust insertion metadata concerning the threat, creating a ledger entry based on the data confidence score and the trust insertion metadata, and using the ledger entry to determine an overall data confidence score for the data confidence fabric. A data threat portfolio view is generated based on the data confidence score and the trust insertion metadata, and the data threat portfolio view is presented to a user.

Abstract: One example method includes building a computing asset that includes a component that has been verified as being ethically sourced, generating a bill of material for the computing asset that identifies the component, adding the bill of material to a data confidence fabric that includes the computing asset as a node, registering the component and the bill of material in the data confidence fabric, and associating a digital fair trade/ethics signature with the component, wherein the digital fair trade/ethics signature is stored in the data confidence fabric in association with the component.

Abstract: Exercising user data rights is disclosed. A rights architecture is provided that can be integrated with a data management system. When data is ingested, the associated data rights are also ingested or applied by default. When data is requested, the request is intercepted, the request is intercepted or processed to determine applicable data rights, which are then enforced as the request is handled in the data management system. The data rights may accompany the data in a header when sold or transferred such that the data rights accompany the data and can be enforced.

Abstract: Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Abstract: A data confidence fabric for generating data confidence scores for a build pipeline is disclosed. Confidence scores are generated for data or jobs in a build pipeline. The scores may be combined into a final confidence score that reflects a confidence in the artifact generated by the pipeline and in the pipeline. A user or infrastructure may or may not perform the artifact based on the associated confidence score.

Abstract: One example method is performed in connection with a data confidence fabric. This method includes generating information about data as the data transits a data confidence fabric, ingesting the information, mapping the ingested information to a timeline, evaluating the timeline, based on the evaluating, generating a recommendation for an action concerning the data, and implementing the action in the data confidence fabric. In this method, the evaluating includes determining if any unexpected time gaps occurred as the data transited the data confidence fabric.

Abstract: One example method includes annotating data with confidence information as the data transits a data confidence fabric, after the data has transited a portion of the data confidence fabric, using the confidence information to generate a data confidence score, assigning the data confidence score to the data, and adjusting the data confidence score. In some cases, the data confidence score may be automatically adjusted downward over time and/or may be adjusted based on input concerning the data.

Abstract: One example method includes formulating a hypothesis for development of computing model, annotating the hypothesis with ethics metadata, storing the hypothesis and the ethics metadata, in association with each other, in a ledger, performing ‘n’ phases of a development lifecycle for the computing model, annotating each of the ‘n’ phases with ethics metadata specific to the phase, updating the ledger to include the ‘n’ phases and the ethics metadata respectively associated with each of the ‘n’ phases, and calculating an ethics confidence score for the computing model.