Abstract: Techniques and a system to facilitate meta-level quantum computation are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor can perform an expectation computation process to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description. The classical processor can execute computer executable components stored in a memory, where the computer executable components comprise a meta-level variational optimization component. The meta-level variational optimization component can perform a meta-level optimization process associated with a k-eigenvalue decomposition process to iteratively determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process.

Abstract: A computer-implemented method for automatic multilabel classification includes receiving a label matrix Y for multiple training instances. The label matrix Y includes multiple labels, each label representing a respective category. The method further includes computing an intermediate matrix YYT, where YT is a transpose of the label matrix Y. The method further includes computing a basis matrix H by a non-negative matrix factorization of the intermediate matrix YYT. The method further includes generating a group testing matrix A by sampling the basis matrix H. The method further includes generating, for each training instance from the training instances, a reduced label vector z by computing a product of the group testing matrix A and a label vector y for respective training instance from the label matrix Y. The method further includes predicting multiple labels associated with an input based on the reduced label vector z.

Abstract: A computer-implemented method is presented for optimal experimental design to correct a misspecified model approximating a behavior of a dynamic system. The method includes formulating an experiment, determining experimental settings for configuring controllable experimental parameters based on mutual information and submodularity, measuring informative values associated with each choice of experimental design, as prescribed by the controllable experimental parameters, and learning a correction function based on the measured informative values. The computer-implemented method further includes determining an experimental design setup for gaining information content, and combining the experimental design setup with the experimental settings to construct a corrected model of the dynamic system.

Abstract: Low-cost techniques for sensing ambient temperatures in a container or package using phase change materials are provided. In one aspect, a temperature sensor is provided. The temperature sensor includes: at least one phase change material configured to transition from an amorphous state to a crystalline state upon exposure to temperatures above a predetermined threshold temperature for a given duration. A method of monitoring temperature exposure of a consumer good using the temperature sensor is also provided.

Abstract: Techniques for automatically eradicating microorganisms (e.g., germs, bacteria, and/or viruses) from appliance handles using ultraviolet (UV) light are provided. In one aspect, a system for eradicating biological contaminants from a handle on an appliance door is provided. The system includes a UV light emitter for producing UV disinfecting light during a cleaning cycle; and a waveguide coating on the handle, coupled to the UV light emitter, for propagating the UV disinfecting light over a surface of the handle. The system can also include a control module for controlling the UV light emitter; and at least one sensor for providing data to the control module as to when to initiate or halt a cleaning cycle. A method for eradicating biological contaminants from a handle on an appliance door using the present system is also provided.

Abstract: A computer-implemented method for analyzing a time-varying graph is provided. The time-varying graph includes nodes representing elements in a network, edges representing transactions between elements, and data associated with the nodes and the edges. The computer-implemented method includes constructing, using a processor, adjacency and feature matrices describing each node and edge of each time-varying graph for stacking into an adjacency tensor and describing the data of each time-varying graph for stacking into a feature tensor, respectively. The adjacency and feature tensors are partitioned into adjacency and feature training tensors and into adjacency and feature validation tensors, respectively. An embedding model and a prediction model are created using the adjacency and feature training tensors. The embedding and prediction models are validated using the adjacency and feature validation tensors to identify an optimized embedding-prediction model pair.

Abstract: Methods and systems for generating output of a simulation model in a simulation system are described. In an example, a processor may retrieve observed output data from a memory. The observed output data may be generated based on a simulation operator of a simulation model. The processor may further optimize a generalization error of a distance measure between the observed output data and model output data. The model output data may be generated based on a high-fidelity operator. The processor may further determine a correction operator based on the optimized generalization error of the distance measure. The processor may further append the correction operator to the simulation operator to produce a supplemented operator. The processor may further generate supplemented output data by applying the simulation model with the supplemented operator on a set of inputs.

Abstract: A dimensionality analysis method, system, and computer program product, include defining a set of symbols along with a grammatical rule that define a syntax of a valid expression having a valid physical dimensionality relationship.

Abstract: An approach is provided in which an information handling system loads a set of encrypted binary code into a processor that has been encrypted based upon a unique key of the processor. The processor includes an instruction decoder that transforms the set of encrypted binary code into a set of instruction control signals using the unique key. In turn, the processor executes a set of instructions based on the set of instruction control signals.

Abstract: In an embodiment, a method includes classifying, using a neural network including quantum components, a data set to generate a first set of classified data. In the embodiment, the method includes generating noise in the quantum components. In the embodiment, the method includes reclassifying, using the neural network, the data set with the generated noise to generate a second set of classified data. In the embodiment, the method includes determining, responsive to comparing the first set of classified data and the second set of classified data, a sensitivity of the quantum components.

Abstract: In an embodiment, a method of sketching using a hybrid quantum-classical system includes creating a set of clustered data sets from a first data set. In an embodiment, the method includes evaluating, using a quantum processor and quantum memory, the set of clustered data sets. In an embodiment, the method includes evaluating, using the quantum processor and quantum memory, a set of quality metrics for the set of clustered data sets. In an embodiment, the method includes reclustering, responsive to at least one of the set of quality metrics failing to meet a quality criterion, the first data set.

Abstract: Implementing a hybrid classical-quantum neural network includes constructing, by at least a first processor, a neural network for classification of input data. The neural network includes a plurality of neural network components. The at least a first processor initiates training of the neural network using training data. The at least a first processor identifies one or more of the plurality of neural network components for replacement. A quantum processor constructs a quantum component corresponding to the one or more network components. The one or more identified neural network components of the neural network are replaced with the quantum component to construct a hybrid classical-quantum neural network.

Abstract: A tensor decomposition method, system, and computer program product include compressing multi-dimensional data by truncated tensor-tensor decompositions.

Abstract: A computer implemented method and computer system for automatically limiting the swing angle of a vehicle door to not hit nearby moving objects, including predicting that a moving object will move to be within a predetermined distance of the door and predicting the distance the moving object will be from the door, determining a swing angle extent of an opening of the door that will avoid hitting the moving object and actuating a door controller to limit the swing angle of the door. Historical data on the speed the door is opened can be used in determining the door swing angle. The current position and velocity of the moving object relative to the door of the vehicle is estimated based on signals received from sensors attached to the vehicle.

Abstract: A tensor decomposition method, system, and computer program product include compressing multi-dimensional data by truncated tensor-tensor decompositions.

Abstract: A computer implemented method and computer system for automatically limiting the swing angle of a vehicle door to not hit nearby moving objects, including predicting that a moving object will move to be within a predetermined distance of the door and predicting the distance the moving object will be from the door, determining a swing angle extent of an opening of the door that will avoid hitting the moving object and actuating a door controller to limit the swing angle of the door. Historical data on the speed the door is opened can be used in determining the door swing angle. The current position and velocity of the moving object relative to the door of the vehicle is estimated based on signals received from sensors attached to the vehicle.

Abstract: Techniques regarding topological classification of complex datasets are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a quantum computing component that can encode eigenvalues of a Laplacian matrix into a phase on a quantum state of a quantum circuit. The computer executable components can also comprise a classical computing component that infers a Betti number using a Bayesian learning algorithm by measuring an ancilla state of the quantum circuit.

Abstract: Hybrid classical-quantum decision maker training includes receiving a training data set, and selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector. A quantum processor applies a quantum feature map to the selected objects to produce one or more output vectors. The first processor determines one or more distance measures between pairs of the output vectors, and determines at least one portion of the quantum feature map to modify the classical feature map. The first processor adds an implementation of the at least one portion of the quantum feature map to the classical feature map to generate an updated classical feature map.

Abstract: Methods and systems for model discovery include forming a mathematical program based on a set of observational data to generate an objective function and one or more constraints. The mathematical program represents a model space as an expression tree comprising operators and operands. The mathematical program is solved by optimizing the objective function subject to the one or more constraints to determine a model in the model space that best fits the set of observational data.

Abstract: A method for correcting perspective distortion includes acquiring a plurality of images of a given subject by at least one camera of a mobile device, wherein each of the images is taken using respective actual settings for the camera captured by at least one sensor of the mobile device. The method further includes processing the images and the respective actual settings for each of the images to synthesize a single image of the given subject, wherein the single image emulates using specified virtual settings for the camera. The method further comprises displaying the synthesized single image of the given subject to a user of the mobile device.