International Business Machines Corporation

E.on Digital Technology Gmbh

Kavitha Hassan Yogaraj

Aaron K Baughman

Gururaja Hebbar

Micah Forster

Shikhar Kwatra

Sudeep Ghosh

Yoshika Chhabra

Tiberiu (Jim) Suto

Christian Eggenberger

Antonio Mezzacapo

Mohit Trivedi

Amit Kumar Raha

Arjun Kashyap

Daniel Joseph Fry

Gabriele Francesco Maria Agliardi

Kumar Jang Bahadur Ghosh

Cortiana Giorgio

Piergiacomo Sabino

Corey O'Meara

Omar Shehab

Markus Van Kempen

Francesco Tacchino

Herman Colquhoun

Souvik Mazumder

Rukhsan Ul Haq

An embodiment includes selecting, using a first attribute of a first object, the first object in a first volumetric video. The embodiment also includes selecting, using a second attribute of a second object, the second object in a second volumetric video, where the first attribute and the second attribute satisfy an aggregation rule. The embodiment also includes generating an aggregated volumetric video from the first volumetric video and the second volumetric video, where the generating of the aggregated video comprises rendering the first object and the second object simultaneously in the aggregated volumetric video based on the aggregation rule.

Personalized aggregation of volumetric videos

An embodiment senses a sequence, responsive to the sensed sequence, segments the sensed sequence into a prompt and text. The embodiment generates an attention embedding representative of the text and computes an attention weight based on the attention embedding. The embodiment generates a prompt embedding representative of the prompt. The embodiment computes a relationship between the prompt embedding and the attention weight comprising correlating the attention embedding and the attention weight based on a beam search; bin packing the prompt embedding where a bin is defined by the attention embedding; matching the prompt embedding to the bin based on a Minkowski distance metric; where the bin packing causes the computing of the relationship between the prompt and the text represented by the attention weight.

Prompt discovery with attention refinement

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.

Generating hybrid quantum-classical neural network architectures

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.

Orchestrating vehicles and remote operating clients

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.

Combined classical/quantum predictor evaluation

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.

Combined classical/quantum predictor evaluation with model accuracy adjustment

A system can comprise a memory that stores computer executable components, and a processor, operably coupled to the memory, that executes the computer executable components comprising: an evaluating component that employs a quantum register to output a processed vector based on a first variable, of set of variables indexed over a selected dimension, and a valuation component that approximates a value function and that, based on the approximating, outputs a valuation amount representing provision of an aspect over the selected dimension, wherein the value function is a function of an inner product of a set of processing functions that are based on the set of variables, including the first variable, wherein a first processing function of the set of processing functions and of the inner product is based on the processed vector, and wherein the set of variables correspond to conditions defining the provision of the aspect.

Portfolio value evaluation through quantum polynomial approximation

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.

Digital and physical experience correlation for product recommendation

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.

Data clustering using analysis of multiple encoding techniques

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Bengaluru, India

Kavitha Hassan Yogaraj