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: A computer-implemented method or protecting a machine-learning model against training data attacks is disclosed. The method comprises performing an initial training of a machine-learning system with controlled training data, thereby building a trained initial machine-learning model and identifying high-impact training data from a larger training data set than in the controlled training data, wherein the identified individual training data have an impact on a training cycle of the training of machine-learning model, wherein the impact is larger than a predefined impact threshold value. The method also comprises building an artificial pseudo-malicious training data set from the identified high-impact training data and retraining the machine-learning system comprising the trained initial machine-learning model using the artificial pseudo-malicious training data set.

Abstract: 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.

Abstract: Systems/techniques that facilitate quantum system selection via coupling map comparison are provided. In various embodiments, a system can access a quantum machine learning (QML) model. In various aspects, the system can identify, from a set of quantum computing systems, a quantum computing system for the QML model, based on a comparison between a first coupling map of the quantum computing system and a second coupling map on which the QML model was trained. If the second coupling map topologically matches the first coupling map or topologically matches a subgraph of the first coupling map, the system can execute the QML model on the quantum computing system. Otherwise, the system can adjust the QML model and can accordingly execute the adjusted QML model on the quantum computing system.

Abstract: A method of generating a classical model to simulate a quantum computational model includes 1) inputting into a quantum computational model a dataset, the quantum computational model being implemented on a quantum computer, 2) computing output results with the quantum computational model using the quantum computer, 3) introducing a variation to at least a portion of the dataset into the quantum computer, 4) computing updated output results of the quantum computational model based on the variation of the at least the portion of the dataset using the quantum computer, and 5) generating a classical twin model of the quantum computational model based on a relationship of the output results and updated output results to the dataset from the quantum computational model.

Abstract: 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.

Abstract: A computer-implemented method, system and computer program product for watermarking quantum models. A precision for a parameter of a quantum model on a quantum computer is determined. The parameter is then truncated based on the determined precision. Furthermore, the watermark is appended in one or more positions of truncated portion of the truncated parameter. In this manner, watermarking quantum models, such as quantum machine learning models, is achieved.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate optimization of quantum-enhanced feature generation are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise a selection component that selects a quantum circuit for mapping a set of classical features to a quantum feature space. The computer executable components further comprise an execution component that provides the quantum circuit for execution by a quantum computer or a quantum simulator to map the set of classical features and to produce quantum-enhanced features. The computer executable components further comprise a training component that assesses selection of quantum circuits and signals the selection component to select a new quantum circuit based on the assessment.

Abstract: 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.

Abstract: A method for enabling a transformation system, comprising a transformation model built for a first setting using first input values, to incorporate second feature values present in a second setting is disclosed. The method comprises providing training input data comprising second feature values as well as expected second results, providing a feature mapper comprising a machine-learning model, wherein output signals of the feature mapper are used as input signals for the transformation system, thereby building a combination of the feature mapper and the transformation model, training of the machine-learning model of the feature mapper using the training input data as input for the feature mapper and using the second results as expected output data of the transformation system, and deploying the combination of the feature mapper and the transformation system as a super machine-learning system comprising a super machine-learning model usable in the second setting.

Abstract: 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.

Abstract: 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.

Abstract: 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.