Abstract: A method for power saving and encryption during analysis of media captured by an information capture device using a partitioned neural network includes replicating, by an information capture device, an artificial neural network (ANN) from a computer server to the information capture device. The ANN on the computer server and a replicated ANN, both, include M layers. The method further includes, in response to captured data being input to be processed, partially processing, by the information capture device, the captured data by executing a first k layers using the replicated ANN, wherein only the k layers are selected to execute on the information capture device. The method further includes transmitting, by the information capture device, an output of the k-th layer to the computer server, which partially processes the captured data by executing the remainder of the M layers using the ANN and the output of the k-th layer.

Abstract: Adapted positioning information is provided towards at least one consuming application regarding a plurality of objects. In a first step, the position of at least one specific object is detected using at least one sensor of a sensor layer or functionality, thereby generating at least one piece of preliminary positioning information. In a second step, subsequent to the first step, a positioning middleware functionality generates at least one piece of adapted positioning information, based on the at least one piece of preliminary positioning information, and based on a priori knowledge. In a third step, subsequent to the second step, the at least one piece of adapted positioning information is provided to the at least one consuming application.

Abstract: A system and method for training a student employ a simulation station that displays output to the student and receives input. The computer system has a rules engine operating on it and computer accessible data storage storing (i) learning object data including learning objects configured to provide interaction with the student at the simulation system and (ii) rule data defining a plurality of rules accessed by the rules engine. The rules data includes, for each rule, respective (a) if-portion data defining a condition of data and (b) then-portion data defining an action to be performed at the simulation station. The rules engine causes the computer system to perform the action when the condition of data is present in the data storage. For at least some of the rules, the action comprises output of one of the learning objects so as to interact with the student. The system may be networked with middleware and adapters that map data received over the network to rules engine memory.

Abstract: Methods and computer-readable media for repeated holdout validation include collecting independent data representing independent variables; collecting dependent data representing a dependent variable; correlating the independent data with the dependent data; creating a data set comprising the correlated independent and dependent data; generating a plurality of unique seeds; creating a plurality of training sets and a plurality of validation sets; associating each training set with a single validation set; training the neural network a plurality of times with the training sets and seeds to create a plurality of models; calculating accuracy metric values for the models using the validation sets associated with the training sets used to create respective models; performing a statistical analysis of the accuracy metric values; and ranking the independent variables by a strength of correlation of individual independent variables with the dependent variable, when a metric of the statistical analysis exceeds a thres

Abstract: An artificial intelligence node including a classical computer, an input channel and an output channel in communication with the classical compute and a quantum computer in two-way communication with the classical computer, wherein the classical computer configures the quantum computer to encode a plurality of word combinations.

Abstract: A data storage device in a mobile body defines a normal world and a secure world and stores a data acquired in the mobile body using a blockchain. The data storage device includes: a normal storage processing unit that stores an acquired data in a storage area of the normal world; a block generator that generates a new block including a hash value based on a last block coupled at last to the blockchain, and a hash value based on the acquired data; and a secure storage processing unit that stores a hash value based on the new block as a block hash value in a storage area of the secure world.

Abstract: Systems and methods of procuring real data items based on user affinity gauged via synthetic data items are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained machine learning model to generate a synthetic data item based on real user data; presenting the synthetic data item to those users; obtaining indications identifying user responses to the synthetic data item; obtaining user-defined control parameters from the users; configuring a user-defined control mechanism to share a portion of the real user data based thereon; obtaining a subset of the real user data based the user-defined control parameters; providing to a particular third-party data source at least one of: data regarding the synthetic data item, the at least one portion of the real user data, and the indications of the users; and then receiving a second real data item from the particular third-party data source.

Abstract: Systems and methods for curation of content, such as e-learning content or online instructional materials, according to particular criteria such as the amount of pictorial representations contained therein, the amount of verbal expression contained therein, and whether the content contains a summary. Other criteria may also be employed. Users may select any one or more of these and other criteria. Content is then sorted according to the selected criteria and presented as an ordered list of content that users can select for viewing.

Abstract: This disclosure relates to methods of constructing efficient quantum circuits for Clifford loaders and variations of these methods following a similar scheme.

Abstract: Systems and methods of modeling the structure and behavior of the quantum continuum based on geometrical principles are provided. In some embodiments, systems and methods of modeling quantum structure and behavior may include modeling a region of space as a three-dimensional projection of a field of N-dimensional hard-spheres, modeling a stable particle within the region of space as a locally stably packed set of hard-spheres, defining an energy subspace comprising one or more additional dimensions, and modeling an energy of the stable particle as an amount of hard-sphere geometry shifted out of the three spatial dimensions into the energy subspace sufficient for the set of hard-spheres to pack stably. Systems and methods for modeling virtual particles and performing quantum communication are also described.

Abstract: Displaying a comparison of genetic data is disclosed, including receiving an indication of a first individual, receiving an indication of a second individual, retrieving the genotypic information for the first individual and the second individual, comparing the genotypic information of the first individual and the second individual, displaying an indication of the comparison of the genotypic information of the first individual and the second individual graphically. A first graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are identical. A second graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are half identical.

Abstract: This disclosure relates to the fields of applied meteorology, in particular to a visualization analysis method of the tropical cyclone forecast verification index data, which comprises the following steps: acquiring tropical cyclone data; obtaining a track forecast verification index and an intensity forecast verification index of the tropical cyclone with the tropical cyclone data; obtaining a visible view of the tropical cyclone track forecast error and a visible view of the tropical cyclone intensity forecast error according to the track forecast verification index and the intensity forecast verification index, and in combination with the geographic information of the tropical cyclone; obtaining a first error and a second error according to the track forecast verification index, and obtaining a joint distribution map of the first error and the second error by using the first error and the second error; obtaining a composite box-shaped histogram through the intensity forecast verification index.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses for applying machine learning techniques as part of registering, for a user, a payment card with at least one account of a merchant. The machine learning technique may be an unsupervised learning classifier that is configured to determine classifications of merchant groups and/or user groups. Based on a classification, the user may be able to select which merchants to register the payment card. Based on the selection, the payment card may be registered with the user's account at the selected merchants. Further, the registration may be performed based on virtual payment card information configured for use with the merchant. The virtual payment card information may be configured to initiate transactions only with the merchant.

Abstract: Techniques are described for distributing, to a distributed network of central stations, alarm events detected in monitoring system data collected by sensors included in monitoring systems located at monitored properties. A system receives monitoring system data collected by sensors included in monitoring systems located at monitored properties, tracks alarm events detected within the monitoring system data, and generates, for central station servers in a distributed network of central stations, load profiles that reflect a volume of alarm events being handled at each of the central station servers at a particular period of time. The system determines capacities to handle additional alarm events for the central station servers, determines relative priorities for the central station remote servers based on the determined capacities, and directs subsequent alarm events to the central station servers based on the relative priorities.

Abstract: An approach is described for a method, product, and apparatus for a machine learning process using weight sharing within a systolic array having reduced memory bandwidth. According to some embodiments, this approach includes providing a systolic array that includes processing elements which each have some number of storage elements for storing weights. For example, the weights can be reused for different data sets by identifying/capturing a current state of the storage elements, generating a plan to transition to a target state of those storage elements, and application of the transition plan such that weights that are already stored in those storage elements can be reused and/or relocate. This lowers the bandwidth requirements for weight memory by allowing weights that have previously been read into the systolic array to be reused.

Abstract: Artificial intelligence based systems and methods are described for analyzing pixel data of an image of a user's body for determining a hair growth direction value of the user's hair. An example method includes aggregating a plurality of training images of a plurality of users' bodies, and training a hair growth direction model operable to output a hair growth direction value. The example method may further include receiving an image of a user comprising pixel data of a portion of the user's body or body area, and analyzing, by the hair growth direction model, the image to determine a user-specific hair growth direction value of the user's hair. The example method may further include generating a product recommendation, wherein the product is designed to address a feature identifiable within the pixel data of the user's body or body area.

Abstract: A diagnostic device (10) includes an acquirer (101) and a diagnoser (140). The acquirer (101) acquires a plurality of input signals including a target signal to be diagnosed for abnormality. The diagnoser (140) diagnoses, using a first index value relating to the target signal and a second index value relating to the plurality of input signals based on a correlation between the plurality of input signals, whether an abnormality occurs. The first index value indicates a degree of similarity of a waveform of the target signal to a predetermined reference waveform. The second index value is a value that is based on comparison between the target signal and a predetermined pattern and is calculated from values of the plurality of input signals.

Abstract: A quantum circuit includes a plurality of Hadamard gates apply Hadamard transforms to a plurality of qubits in a corresponding plurality of initial states. A plurality of weighted oracle gates sequentially call a weighted oracle operator on the plurality of qubits to produce a sequence of quantum oracle calls, wherein the weighted oracle operator for the plurality of qubits applies an adjustable phase rotation at each of the quantum oracle calls in the sequence of quantum oracle calls. A plurality of diffusion gates apply a plurality of diffusion operators, wherein a selected one or more of a plurality of diffusion operators is applied after each of the quantum oracle calls in the sequence of quantum oracle calls. A measurement function generates a quantum computing result based on a measurement from the plurality of qubits, after the sequence of quantum oracle calls are applied and after the plurality of diffusion operators are applied.

Abstract: Disclosed is a quantum computing system including a first quantum chip including first physical qubits, a second quantum chip including second physical qubits, and a management device. The management device includes a physical qubit layer that manages physical qubit mapping including information about physical channels between first and second physical qubits, an abstraction qubit layer that manages abstraction qubit mapping including information about abstraction qubits and abstraction channels between the abstraction qubits based on the physical qubit mapping, a logical qubit layer that divides the abstraction qubits into logical qubits and to manage logical qubit mapping including information about logical channels between the logical qubits, based on the abstraction qubit mapping, and an application qubit layer that allocates at least one logical qubit corresponding to a qubit request received from a quantum application program based on the logical qubit mapping.

Abstract: A superconducting device includes two nodes and a Josephson junction coupled between the two nodes, wherein the Josephson junction is characterized by a superconducting phase difference, ?, wherein the superconducting device has a potential that varies as a function of the superconducting phase difference, ?, and has a single potential well. The potential has a non-zero cubic term and quartic term is zero. The Josephson junction may be a single small Josephson junction. The superconducting device may include a superconducting ring connected between the two nodes. The superconducting ring may include a first ring portion with a plurality of large Josephson junctions connected in series. The superconducting ring may also include a second ring portion that includes the single small Josephson junction in parallel with the plurality of large Josephson junctions between the two nodes.

Abstract: A triage system that determines whether a user is likely to have contracted a disease based on sensor data received from a user device (e.g., a smartphone or activity tracker). Each symptom is identified by comparing sensor data to a predetermined baseline and comparing the difference to a predetermined symptom threshold. Because direct measurement of symptoms using the sensors available to the user may not be feasible or sufficiently accurate, the triage system also uses surrogates the identify certain symptoms. For example, a fever may be identified using heart data, a cough or shortness of breath may be identified by analyzing recorded sound, fatigue may be identified by analyzing the movement of the user device, and loss of smell or taste may be identified by recording sound and using speech detection algorithms to identify phrases in the recorded sound indicative of loss of smell or taste.

Abstract: Systems and methods that address an optimized method to have a visual representation of qubit stochastic errors. A visual representation is generated of qubit stochastic errors that provides a platform to analyze impact on performance of a quantum circuit to facilitate understanding how noise and error impacts circuit results. Stochastic errors accumulated throughout a circuit are visualized using a gradient overlay.

Abstract: The disclosure describes various aspects related to enabling effective multi-qubit operations, and more specifically, to techniques for enabling parallel multi-qubit operations on a universal ion trap quantum computer. In an aspect, a method of performing quantum operations in an ion trap quantum computer or trapped-ion quantum system includes implementing at least two parallel gates of a quantum circuit, each of the at least two parallel gates is a multi-qubit gate, each of the at least two parallel gates is implemented using a different set of ions of a plurality of ions in a ion trap, and the plurality of ions includes four or more ions. The method further includes simultaneously performing operations on the at least two parallel gates as part of the quantum operations. A trapped-ion quantum system and a computer-readable storage medium corresponding to the method described above are also disclosed.

Abstract: An urban remote sensing image scene classification method in consideration of spatial relationships is provided and includes following steps of: cutting a remote sensing image into sub-images in an even and non-overlapping manner; performing a visual information coding on each of the sub-images to obtain a feature image Fv; inputting the feature image Fv into a crossing transfer unit to obtain hierarchical spatial characteristics; performing convolution of dimensionality reduction on the hierarchical spatial characteristics to obtain dimensionality-reduced hierarchical spatial characteristics; and performing a softmax model based classification on the dimensionality-reduced hierarchical spatial characteristics to obtain a classification result.

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples.

Abstract: Provided are a system, method, and computer program product for optimizing a manufacturing process. The method includes receiving manufacturing data associated with a manufacturing process for manufacturing a product. The manufacturing data may include data from a plurality of data sources associated with a plurality of stages of the manufacturing process, and the manufacturing data may include values for a plurality of parameters including at least one process parameter value and at least one quality parameter value. The method includes generating a time-sequenced data structure including the manufacturing data and transforming the time-sequenced data structure to a positionally-dimensioned data structure based on timing data associated with the plurality of stages. The method includes determining a new value for the at least one process parameter value based on the positionally-dimensioned data structure and at least one algorithm and optimizing the manufacturing process based on the new value.

Abstract: Methods, systems, and apparatus for improving recommendation systems. In one aspect, a method includes obtaining training data including data sets, wherein each data set includes a value that corresponds to the target feature and multiple values that each correspond to a respective input feature of a set of input features; assigning an input feature from the set of input features to a root node of the quantum decision tree based on calculated information gain values for the input features; creating a path from the root node by iteratively: calculating a cumulative information gain value for unassigned input features; identifying a maximal cumulative information gain value for the unassigned input features and assigning the unassigned input feature corresponding to the maximal cumulative information gain value to a current leaf node in the path creating a new leaf node.

Abstract: A scheduling algorithm for scheduling training of deep neural network (DNN) weights on processing units identifies a next job to provisionally assign a processing unit (PU) based on a doubling heuristic. The doubling heuristic makes use of an estimated number of training sets needed to complete training of weights for a given job and/or a training speed function which indicates how fast the weights are converging. The scheduling algorithm solves a problem of efficiently assigning PUs when multiple DNN weight data structures must be trained efficiently. In some embodiments, the training of the weights uses a ring-based message passing architecture. In some embodiments, performance using a nested loop approach or nested loop fashion is provided. In inner iterations of the nested loop, PUs are scheduled and jobs are launched or re-started. In outer iterations of the nested loop, jobs are stopped, parameters are updated and the inner iteration is re-entered.

Abstract: Example circuit implementations of Szegedy's quantization of the Metropolis-Hastings walk are presented. In certain disclosed embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which a quantum move register is reset at every step in the quantum walk. In further embodiments, a quantum walk procedure of a Markov chain Monte Carlo simulation is implemented in which an underlying classical walk is obtained using a Metropolis-Hastings rotation or a Glauber dynamics rotation. In some embodiments, a quantum walk procedure is performed in the quantum computing device to implement a Markov Chain Monte Carlo method; during the quantum walk procedure, an intermediate measurement is obtained; and a rewinding procedure of one or more but not all steps of the quantum walk procedure is performed if the intermediate measurement produces an incorrect outcome.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate estimation of an expected energy value of a Hamiltonian based on data of the Hamiltonian, the quantum state produced by a quantum device and/or entangled measurements 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 selection component that selects a quantum state measurement basis having a probability defined based on a ratio of a Pauli operator in a Hamiltonian of a quantum system. The computer executable components can further comprise a measurement component that captures a quantum state measurement of a qubit in the quantum system based on the quantum state measurement basis.

Abstract: An artificial intelligence system within a network including a plurality of interconnected nodes including a first node and a second node, containing a set of interconnected nodes, a “network,” in which some nodes are externally connected, and all nodes are connected to a subset of the other nodes in such a way that the output channel of one node is connected to the input channel of another via a two-way connection, and where each node has access to the system clock, and has a classical computer (CC), and has a quantum computer (QC), and has one or more input channels, each capable of sending or receiving signals, and has one or more output channels, each capable of sending or receiving signals, and can perform a quantum computation cycle.

Abstract: Computer implemented methods and systems generate an improved predicted model of an industrial process or process engineering system. The model is a function of measurable features of the subject process and selected first principle features. First principle features are selected that capture linearities in a residual of a linear model constructed using a received dataset of the subject process. The model can further be a function of a scaled spline. The scaled spline is generated by computing a spine for a measurable feature of the subject process, fitting the computer spline to the residual of the constructed linear model, and scaling the fitting spline with a scaling factor. The model results in improved predictions of behavior of the subject process by relying primarily on the data of the measurable features of the subject process.

Abstract: The present disclosure provides methods and systems for performing non-classical computations. The methods and systems generally use a plurality of spatially distinct optical trapping sites to trap a plurality of atoms, one or more electromagnetic delivery units to apply electromagnetic energy to one or more atoms of the plurality to induce the atoms to adopt one or more superposition states of a first atomic state and a second atomic state, one or more entanglement units to quantum mechanically entangle at least a subset of the one or more atoms in the one or more superposition states with at least another atom of the plurality, and one or more readout optical units to perform measurements of the superposition states to obtain the non-classical computation.

Abstract: Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.

Abstract: A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

Abstract: Embodiments of the disclosed technology concern transforming a high-level quantum-computer program to one or more symbolic expressions. Because the transformations lead to symbolic expressions in the compiled code, one can extract these to arrive at symbolic resource estimates for the quantum program. In cases where these transformations do not yield closed-form solutions, they can still be evaluated many orders of magnitude faster than it was possible using other resource estimation tools. Having access to such symbolic or near-symbolic expressions not only greatly improves the performance of accuracy management and resource estimation, but also better informs quantum software developers of the bottlenecks that may be present in the quantum program. In turn, the underlying quantum-computer program can be improved as appropriate.

Abstract: Systems and methods for the optical control of qubits and other quantum particles with spatial light modulators (SLM) for quantum computing and quantum simulation are disclosed herein. The system may include a particle system configured to provide an ordered array comprising a multiplicity of quantum particles or a multiplicity of qubits, an optical source, a SLM configured to project a structured illumination pattern capable of individually addressing one or more quantum particles or qubits of the ordered array, and a SLM controller.

Abstract: Provided is a three-dimensional (3D) transmon qubit apparatus including a body portion, a driver, a transmon element disposed in an internal space of the body portion, a first tunable cavity module disposed in the internal space of the body, and comprising a first superconductive metal panel; and a second tunable cavity module disposed in the internal space of the body, and comprising a second superconductive metal panel, wherein the transmon element is disposed between the first superconductive metal panel and the second superconductive metal panel; wherein the first tunable cavity module and the second tunable cavity module are configured to adjust a distance between the first superconductive metal panel and the second superconductive metal panel, and wherein the driver is configured to tune a resonance frequency by adjusting a 3D cavity by adjusting the distance between the first superconductive metal panel and the second superconductive metal panel.

Abstract: A model analyzer may receive a representative data set as input and select one of a plurality of analytic models to perform the analysis. Before deciding which model to use the model may be trained, and the trained model evaluated for accuracy. However, some models are known to behave poorly when the training data is distributed in a particular way. Thus, the cost of training a model and evaluating the trained model can be avoided by first analyzing the distribution of the representative data. Identifying the representative data distribution allows ruling out use of models for which the distribution of the representative data is unsuitable. Only models that may be compatible with the distribution of the representative data may be trained and evaluated for accuracy. The most accurate trained model whose accuracy meets an accuracy threshold may be selected to analyze subsequently received data related to the representative data.

Abstract: Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

Abstract: Methods and systems for performing a surface code error detection cycle. In one aspect, a method includes initializing and applying Hadamard gates to multiple measurement qubits; performing entangling operations on a first set of paired qubits, wherein each pair comprises a measurement qubit coupled to a neighboring data qubit in a first direction; performing entangling operations on a second set of paired qubits, wherein each pair comprises a measurement qubit coupled to a neighboring data qubit in a second or third direction, the second and third direction being perpendicular to the first direction, the second direction being opposite to the third direction; performing entangling operations on a third set of paired qubits, wherein each pair comprises a measurement qubit coupled to a neighboring data qubit in a fourth direction, the fourth direction being opposite to the first direction; applying Hadamard gates to the measurement qubits; and measuring the measurement qubits.

Abstract: A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Abstract: A transition control unit detects, when stochastically determining based on a temperature, energy changes, and a random number whether to accept any of a plurality of state transitions according to a relative relationship between the energy changes and thermal excitation energy, a minimum value among the energy changes. The transition control unit then subtracts, when the minimum value is positive, an offset obtained by multiplying the minimum value by a value M that is greater than 0 and less than or equal to 1 from each of the energy changes corresponding to the plurality of state transitions.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate determining a state of a qubit are described. 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 relation determining component that can determine relation of a status signal of a quantum computing device to a noise value of the quantum computing device. The system can further include an operation time estimator that can estimate an operation time for the quantum computing device based on the relation of the status signal to the noise value.

Abstract: K-NN spatial queries may be performed, and the results of one K-NN query are re-used to perform a plurality of K-NN queries on a set of query points. More specifically, a K-NN query is performed on a pre-defined location, for example, an object point, to find the nearest K data objects for this pre-defined location, the results of this K-NN query being used to perform further K-NN queries on the query points near the pre-defined location. In doing so, the efficiency of large scale K-NN spatial queries is improved by limiting the spatial search range of the K-NN queries to be performed and re-using pre-computed K-NN data.

Abstract: A system for attention-based layered neural network classification is provided. The system comprises: a sequence of layered neural networks; and a controller configured for controlling data routed through the sequence of layered neural networks, the controller configured to: receive interaction data comprising data features, wherein the data features are distinct characteristics of the interaction data; input data features into the sequence of layered neural networks, wherein each sequential layer of the sequence of layered neural networks comprises a heightened rigor level for at least one of the data features; calculate a relevance score output for at least one of the data features at each layer of the sequence of layered neural networks; and integrate the relevance score output from each layer of the sequence of layered neural networks to generate a total relevance score output.

Abstract: A quantum computing system computes soft information quantifying an effect of soft noise on multiple rounds of a syndrome measurement that is output by a quantum measurement circuit. The soft noise arises due to imperfections in a readout device that introduce variability in repeated measurements of ancilla qubits and is distinct from quantum noise arising from bit-flips in data qubits that are indirectly measured by the ancilla qubits. The quantum computing system applying decoding logic to identify fault locations within the quantum measurement circuit based on the computed soft information.

Abstract: Methods and systems are provided for belief space planning for multiple robots at an initial belief state, for reducing belief space uncertainty, including receiving multiple, candidate, multi-robot paths, each including one or more pose constraints; generating a candidate factor graph for each candidate multi-robot path, and generating from each candidate factor graph a topology graph; calculating a signature metric from each topology graph, wherein the signature metric is one of a Von Neumann (VN) metric and a spanning tree (ST) metric; selecting a multi-robot path having the greatest signature metric from among the candidate, multi-robot paths; and instructing the multiple robots to proceed according to the selected multi-robot path.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Systems and methods are described to accurately extract device parameters and optimize the design of macroscopic superconducting structures, for example qubits. This method presents the advantage of reusing existing plaquettes to simulate different processor topologies. The physical elements of a qubits are extracted via plurality of plaquettes. Each plaquette contains at least one physical element of the qubit design and has two ports on each side. Each plaquette is concatenated to at least one other plaquette via two ports. The values of inductance (L), capacitance (C) and mutual inductance (M) and quantum critical point of the qubit design can be computed. Changing the physical elements of the qubit design and iterating the method allows to effortlessly refine the qubit design.