International Business Machines Corporation

Kyndryl, Inc.

Frederik Frank Flöther

Stefan Ravizza

Erik Rueger

Andrea Giovannini

Shikhar Kwatra

Tim Uwe Scheideler

Florian Graf

Patrick Lustenberger

Robert E Loredo

Vinod A Valecha

Jared Andrew Limberg

Paul R Bastide

Saurabh Yadav

Matthias Biniok

David Ocheltree

Freddy Lorge

Stefan A G Van Der Stockt

Cameron Donald Ferstat

Vaibhaw Kumar

David Mesterhazy

Eric Slottke

Thomas Pfeiffer

Markus Brandes

Maximilian Ott

Michele Grossi

Georgios Chaloulos

A processor may gather raw data comprising a plurality of characteristic data samples of a target user group. The processor may categorize the characteristic data samples into a plurality of user-related classes and triggers. The processor may build an input property graph for each characteristic data sample. The processor may augment the input property graph by a concept of hierarchies. The processor may determine a modification vector from the augmented input property graph. The processor may train an encoder/decoder combination machine-learning system. An embedding vector and a modification vector are used as input for the decoder to build a trained machine-learning generative model.

Modification and generation of conditional data

In an approach, a processor creates a multi-layered knowledge graph (KG), wherein a first layer is a core KG, a second layer has application-specific structured facts, and a third layer has individualized facts. A processor adapts weights in each layer of the multi-layered KG based on the individualized facts. A processor uses, as input data to the multi-layered KG, individual environmental data. A processor maps the input data to the multi-layered KG in a sequence of the first layer, the second layer, and the third layer. A processor selects, as relevant nodes in the first layer and the second layer, the relevant nodes lying on a selected path from the input data via the first layer, the second layer, and the third layer having the highest average weight value along the selected path. A processor outputs facts of the relevant nodes from the first layer and the second layer.

Environment augmentation based on individualized knowledge graphs

To obtain meaningful computational results despite limits on the amount of data that can be input to a quantum computer, a data selection system uses an iterative approach to select a suitable subset of data to be input to a quantum device for processing by a quantum algorithm. The system compresses and clusters a data set according to a task-specific distribution criteria and selects a subset of this clustered data corresponding to representative cases of the data. The selected subset is processed by the quantum device and the system generates a metric score based on the degree to which the results satisfy a performance criterion. The selected subset is refined over multiple iterations based on successive metric scores until a termination criterion is reached, and the final selected subset of data is used as input to the quantum computer for execution of the processing task.

Optimized selection of data for quantum circuits

Systems and techniques that facilitate quantum circuit valuation are provided. In various embodiments, a system can comprise an input component that can access a first quantum circuit. In various embodiments, the system can further comprise a valuation component that can appraise the first quantum circuit based on one or more factors (e.g., frequency factor, complexity factor, resource factor, similarity factor), thereby yielding a value score that characterizes the first quantum circuit. In various instances, the system can further comprise an execution component that can recommend deployment of the first quantum circuit based on determining that the value score exceeds a threshold.

Quantum circuit valuation

Apparatuses, system, and computer-implemented methods to facilitate physical representation of quantum entanglement are provided. According to an embodiment, an apparatus can comprise two or more qubit representation devices that interact with one another in a defined manner, and a display component operatively coupled to at least one of the two or more qubit representation devices, wherein the display component outputs a visual indicator of quantum entanglement between the two or more qubit representation devices.

Physical representation of quantum entanglement

An embodiment includes processing a dataset to generate a set of feature vectors that include a first feature vector corresponding to a first concept within a user's areas of interest and a second feature vector corresponding to a second concept within the user's areas of study. The embodiment identifies clusters of the feature vectors and identifies key features that most contribute to influencing the clustering algorithm. The embodiment selects the first feature vector in response to a user query, and then selects the second feature vector based on an overlap between key features of the first and second feature vectors and a degree of dissimilarity between the first and second concepts. The embodiment outputs a query response that includes the second concept. The embodiment also determines an effectiveness value based on sensor data indicative of a user action responsive to the outputting of the response to the query.

Cognitive generation of tailored analogies

A computer-implemented method for managing updates to a knowledge graph with incoming change requests is disclosed. The method comprises creating a snapshot of the knowledge graph, building sub-regions of the knowledge graph, classifying each incoming change to the knowledge graph so that it relates to one of the sub-regions, creating at least one change layer, wherein each of the at least one change layer relates to one of the sub-regions, wherein each of the at least one change layer comprises a portion of the incoming change requests, closing the at least one change layer upon reaching a predetermined threshold value for the at least one change layer, and integrating the at least one change layer into the knowledge graph, thereby generating a updated knowledge graph.

Knowledge graph update using change layers

A computer-implemented method for generating a group of representative model cases for a trained machine learning model may be provided. The method comprising determining an input space, determining an initial plurality of model cases, and expanding the initial plurality of model cases by stepwise modifying field values of the records representing the initial plurality of model cases resulting in an exploration set of model cases. Additionally, the method comprises obtaining a model score value for each record of the exploration set of model cases, continuing the expansion of the exploration set of model cases thereby generating a refined model case set, and selecting the records in the refined model case set based on relative record distance values and related model score values between pairs of records, thereby generating the group of representative model cases.

Artificial intelligence transparency

A method for personalizing a message between a sender and a receiver is provided. The method comprises semantically analyzing a communication history to form a knowledge graph, deriving formality level values using a first trained ML model, analyzing parameter values of replies to determine receiver impact score, and training a second ML system to generate a model to predict the receiver impact score value. The method also comprises selecting a linguistic expression in a message being drafted, determining an expression intent, modifying the linguistic expression based on the formality level and the expression intent to generate a modified linguistic expression, and testing whether the modified linguistic expression has an increased likelihood of a higher receiver impact score. The method also comprises repeating selecting the linguistic expression, determining the expression intent, modifying the linguistic expression, and testing until a stop criterion is met.

Communication content tailoring

A quantum state visualization device comprising at least a portion of a spherical shell and a support structure affixed to the spherical shell. A portion of the support structure can intersect a center of the spherical shell. The quantum state visualization device further comprising a indicator pivotally attached to the support structure at the portion of the support structure intersecting the center of the spherical shell. The indicator is representative of a quantum state based on its position relative to the spherical shell.

Quantum state visualization device

Aspects of the present invention disclose a method for verifying labels of records of a dataset. The records comprise sample data and a related label out of a plurality of labels. The method includes one or more processors dividing the dataset into a training dataset comprising records relating to a selected label and an inference dataset comprising records with sample data relating to the selected label and all other labels out of the plurality of labels. The method further includes dividing the training dataset into a plurality of learner training datasets that comprise at least one sample relating to the selected label. The method further includes training a plurality of label-specific few-shot learners with one of the learner training datasets. The method further includes performing inference by the plurality of trained label-specific few-shot learners on the inference dataset to generate a plurality of sets of predicted label output values.

Data label verification using few-shot learners

A method and a related system for allocating target locations to optimize trajectories between several objects and the target locations may be provided. The method comprises capturing location data of the target locations as well as location and movement data of the objects, building a graph using the target locations as well as the location and movement data and integrating constraints into the graph. Furthermore, the method comprises determining for each of the several objects a desired target location using a first optimization system, thereby determining endpoints of a trajectory between each of the objects and its respective desired target location and selecting for each of the several objects an optimal path as the trajectory between the object and the desired target location, using a second optimization system, and taking into account movements of other objects along their trajectories.

Graph enabled location optimization

Systems, computer-implemented methods, and computer program products to facilitate quantum platform routing of a quantum application component are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a dissection component that identifies one or more components of a quantum application. The computer executable components can further comprise a determination component that selects at least one quantum platform to execute the one or more components of the quantum application based on a defined run criterion.

Quantum platform routing of a quantum application component

A method for linking a first knowledge graph (KG) and a second KG in the presence of a third KG is provided. Content of nodes of the first KG is compared to nodes of the second KG. If a first KG node has a content relationship to a related second KG node, an edge identified by a tuple identifying the first KG and the first KG node and a tuple identifying the second KG and the second KG node is stored in a meta-layer KG. The method comprises comparing content of the nodes from the third KG with the content of nodes from the first and second KG, and in case relationships are identified, more complex tuples establishing this relationship in the meta-layer are stored. Finally, the method also comprises storing at least all nodes and edges of the meta-layer knowledge graph.

Growing community of inventors

Schlieren, Switzerland

Frederik Frank Flöther