Abstract: An apparatus comprises a processing device configured to train first and second machine learning models utilizing a first training dataset comprising inputs each associated with a class label of one of a set of classes and a second training dataset comprising distilled representations of the two or more classes, and to identify candidate adversarial example inputs utilizing the trained first and second machine learning models. The processing device is further configured to determine whether the candidate adversarial example inputs are true positive adversarial example inputs based on a confidence-aware clustering and to generate an updated first training dataset comprising corrected class labels for the true positive adversarial example inputs and an updated second training dataset comprising updated distilled representations determined utilizing the corrected class labels.

Abstract: Methods, apparatus, and processor-readable storage media for automatically processing user request data using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating, using a chatbot during a communication session with a user, answers to user requests by processing the user requests using a first set of artificial intelligence techniques associated with the chatbot; generating knowledge base-related predictions associated with the user requests by processing the user requests using a second set of artificial intelligence techniques associated with a knowledge base; calculating at least one score associated with at least a portion of the answers and the user requests based on qualifying values computed in connection with at least one of generating the answers and generating the knowledge base-related predictions; and performing one or more automated actions based on the at least one calculated score.

Abstract: An apparatus comprises a processing device configured to utilize a first machine learning model to determine a classification output for a given input indicating probability of the given input belonging to each of a set of classes, and to utilize a second machine learning model to determine a clustering output for the given input indicating which of a set of clusters that the given input belongs to, the clusters corresponding to respective ones of the classes. The processing device is further configured, responsive to determining that the given input represents an adversarial example based at least in part on a comparison of the classification and clustering outputs for the given input, to modify subsequent processing of the given input by additional machine learning models.

Abstract: Techniques for integrating a trusted execution platform with a function-based service framework are disclosed. For example, a method comprises reading configuration information identifying at least one data providing party and at least one function providing party, and generating, based at least in part on the configuration information, an enclave comprising a circuit configured to execute a function. The method further comprises receiving in the enclave and via at least a first secure communication channel, the function from the at least one function providing party, and receiving in the enclave and via at least a second secure communication channel, data from the at least one data providing party. The function and the data are sent to the circuit, wherein the circuit executes the function to compute at least one output based at least in part on the data.

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: 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: 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 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: A method comprises generating a distilled dataset from an input dataset, wherein the input dataset comprises a plurality of first data samples, and the distilled dataset comprises a plurality of second data samples. In the method, an input data sample is received, and similarities are computed between the input data sample and respective ones of the plurality of second data samples. The method further comprises selecting one or more second data samples of the plurality of second data samples based, at least in part, on the computed similarities, and retrieving one or more first data samples of the plurality of first data samples from the input dataset based, at least in part, on the selected one or more second data samples. The generating is performed, at least in part, using one or more machine learning models.

Abstract: Techniques for integrating a trusted execution platform with a multi-party computation framework are disclosed. For example, a method comprises receiving a plurality of keys from a plurality of parties, wherein respective ones of the plurality of keys correspond to respective ones of the plurality of parties. The respective ones of the plurality of keys are used in connection with establishing one or more secure channels for communicating with the respective ones of the plurality of parties. The method further comprises receiving respective data inputs from the respective ones of the plurality of parties over the one or more secure channels, and sending the respective data inputs to a computation function to compute at least one output based on the respective data inputs. The at least one output is sent over the one or more secure channels to at least one party of the plurality of parties.

Abstract: One example method includes transmitting a request for a container image to a registry, receiving metadata associated with the container image, wherein the metadata allows a controller to mount an empty filesystem on a host machine, starting a container from the container image without receiving all files associated with the container image, receiving files, from a container server, needed by the container based on an access sequence associated with the container. This allows a container to be started without downloading the entire container image and also conversed bandwidth by providing the files as needed based on the manner in which the container accesses files during execution.

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 one or more remote storage service instances attached to the second processing device, and to initiate storage emulation modules for the remote storage service instances attached to the second processing device, the storage emulation modules emulating one or more physical storage devices configured for attachment to the second processing device.

Abstract: Techniques for function execution environment selection for a decomposed application are provided. In one example, an apparatus comprises at least one processing platform configured to execute a portion of an application program in a first virtual computing element, wherein the application program comprises one or more portions of marked code, receive a request for execution of one of the one or more portions of marked code, decide whether to execute the portion of marked code identified in the request in the first virtual computing element or in a second virtual computing element, determine an execution environment from one or more execution environments specified in the marked code for the second virtual computing element to execute the marked code, when it is decided to execute the portion of the marked code in the second virtual computing element, and cause the portion of marked code identified in the request to be executed.

Abstract: Techniques are disclosed for 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 identifying, based on one or more metrics, at least one cluster from a plurality of clusters as an execution environment in which the machine learning model-based request is to be executed. The machine learning model-based request may then be forwarded to the at least one identified cluster for execution.

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: 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 one or more remote storage service instances attached to the second processing device, and to initiate storage emulation modules for the remote storage service instances attached to the second processing device, the storage emulation modules emulating one or more physical storage devices configured for attachment to the second processing device.

Abstract: One example method includes receiving a computation workflow defined by a graph that includes quantum computing nodes, receiving a catalogue of quantum computing instances that are available in a hybrid classic-quantum computation infrastructure, transforming the graph to create a first graph transformation, and each of the quantum computing nodes is assigned a respective candidate resource allocation that identifies candidate resources operable to execute a respective quantum algorithm associated with that quantum computing node, and the transforming is performed using information from the catalogue, and optimizing the computation workflow by selecting, for each of the quantum computing nodes, a resource from the candidate resource allocation associated with that quantum computing node, and the optimizing includes transforming the first graph transformation to create a second graph transformation that specifies the selected resources for each node.