Abstract: A computer-implemented method for orchestrating operation of multiple vehicles includes generating a single display image stream based on multiple image streams, each image stream corresponding to a respective vehicle, the vehicles being assigned to be controlled by a remote operating client. The method further includes receiving, from the remote operating client, a vehicle operation instruction in response to the single display image stream being displayed by the remote operating client. The method further includes transmitting, to at least one vehicle from the multiple vehicles, the operation instruction to manipulate operation of the at least one vehicle.

Abstract: A computer-implemented method for orchestrating operation of multiple vehicles includes detecting, based on a monitoring of a first remote operating client assigned to control the multiple vehicles, that the first remote operating client is to be replaced. Further, the method includes identifying, from multiple remote operating clients, a second remote operating client as a replacement of the first operating client, the identifying includes determining compatibility of the second remote operating client with the multiple vehicles. Further, the method includes reassigning control of the multiple the vehicles to the second remote operating client, which provides an operation instruction to manipulate operation of the multiple vehicles.

Abstract: Provided are a computer-implemented method, a system, and a computer program product for futureproofing a machine learning model, in which historical data for updates and changes to a baseline machine learning model are received. A futureproofing metric is generated. An enhanced machine learning model comprising a futureproofed version of the baseline machine learning model is generated with the historical data and the baseline machine learning model as inputs.

Abstract: Provided are techniques for enhancing silent features with adversarial networks for improved model versions. Input features are obtained. Hidden features are identified. Quantum feature importance scoring is performed to assign an importance score to each of the hidden features. Silent features are identified as the hidden features with the importance score below a first threshold. Important features are identified as the input features and as the hidden features with the importance score above a second threshold. A silent feature model is built using the silent features. An important feature model is built using the important features. An ensemble model is built with the silent feature model and the important feature model. The ensemble model is used to generate one or more predictions and one or more prescriptions.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: Aspects of the invention include systems and methods configured for hyper-personalized feature morphing of an avatar within a metaverse. A non-limiting example computer-implemented method includes predicting a next interaction for the avatar and comparing one or more current features of the avatar to one or more feature requirements for the predicted next interaction. The method further includes determining, based on the comparison, that one or more current features of the avatar do not match one or more feature requirements for the predicted next interaction. Responsive to the determination, features of the avatar are altered to satisfy the one or more feature requirements for the predicted next interaction.

Abstract: Providing a hybrid neural network architecture by training a plurality of models using a set of training data, the plurality comprising quantum models and classical models, evaluating each model using a common test data set, assigning one or more evaluation metrics to each model according to the evaluation, generating a plurality of networks, each network comprising a combination of the models, evaluating a flow of each network, selecting a network according to the flow, and providing the selected network to a user.

Abstract: A price for use of a digital asset in a set of digital assets is determined. The set of digital assets stored in a digital asset repository. A time slot during which the digital asset is available for use is determined. The digital asset is leased out at the price and during the time slot, the leasing allowing use of the digital asset during the time slot in return for payment of the price. the leased digital asset is integrated with a set of base characteristics of a virtualized user. The integrated leased digital asset is presented in a virtual environment during the time slot.

Abstract: Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Abstract: Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.