Abstract: Described herein is a simulation of an input quantum circuit, comprising a machine-readable specification of a quantum circuit. Aspects include partitioning the input quantum circuit into a group of sub-circuits based on at least two groups of qubits identified for tensor slicing, wherein the resulting sub-circuits have associated sets of qubits to be used for tensor slicing. The simulating can occur in stages, one stage per sub-circuit. A set of qubits associated with a sub-circuit can be used to partition the simulated quantum state tensor for the input quantum state circuit into quantum state tensor slices, and the quantum gates in that sub-circuit can used to update the quantum state tensor slices into updated quantum state tensor slices. The updated quantum state tensor slices are stored to secondary storage as micro slices.

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: Described herein is a simulation of an input quantum circuit, comprising a machine-readable specification of a quantum circuit. Aspects include partitioning the input quantum circuit into a group of sub-circuits based on at least two groups of qubits identified for tensor slicing, wherein the resulting sub-circuits have associated sets of qubits to be used for tensor slicing. The simulating can occur in stages, one stage per sub-circuit. A set of qubits associated with a sub-circuit can be used to partition the simulated quantum state tensor for the input quantum state circuit into quantum state tensor slices, and the quantum gates in that sub-circuit can used to update the quantum state tensor slices into updated quantum state tensor slices. The updated quantum state tensor slices are stored to secondary storage as micro slices.

Abstract: Techniques and a system for quantum circuit decomposition by integer programming are provided. In one example, a system includes a quantum circuit decomposition component and a simulation component. The quantum circuit decomposition component generates graphical data for a quantum circuit that is indicative of a graphical representation of the quantum circuit. The graphical representation is formatted as a hypergraph. The simulation component simulates the quantum circuit based on the graphical data associated with the hypergraph.

Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling 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 scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.

Abstract: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling 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 scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.

Abstract: Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.

Abstract: Mechanisms are provided for conducting a natural language dialogue between the automatic dialogue system and a user of a client computing device. An automatic dialogue system receives natural language text corresponding to a user input from the user via the client computing device, the natural language text having an ambiguous portion of natural language text. The automatic dialogue system analyzes user profile information corresponding to the user to identify an anomaly in the user profile information and predicts a user intent associated with the anomaly. The automatic dialogue system disambiguates the ambiguous portion of the natural language text based on the predicted user intent and generates a response to the user input based on the disambiguated natural language text which is output to the client computing device to thereby conduct the natural language dialogue.

Abstract: A first feature set for an accident is identified, based on one or more historical accident reports. The accident is geolocated to obtain road data for a location of the accident. Weather data for the accident is obtained, based on the accident location. Hidden data for the accident is obtained, based on the accident location and the time of the accident. The first feature set is combined with the road data, weather data, and hidden data to produce a complete feature set for the accident. A model feature set is generated, based on a combination of the complete feature set and a plurality of other complete feature sets produced based on historical accident reports for a plurality of other accidents.

Abstract: Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.

Abstract: Mechanisms are provided for customizing responses to future questions based on identified anomalies in user profile information. An automated dialogue system monitors information associated with a plurality of entities, where the information includes quantities for variable values associated with the entities. The automated dialogue system, in response to determining that a quantity of a variable value associated with an entity in the plurality of entities has changed by an amount equal to or exceeding a corresponding threshold value, generates response information associated with a quantity of the variable value and an entity to respond to at least one future question. In addition, the automated dialogue system stores the responsive information in association with the entity for later retrieval in response to initiation of a dialogue session with the automated dialogue system. Time thresholds may be established for determining when to stop using the responsive information for responding to questions.

Abstract: Methods and systems for locking a cache line of a cache. A cache line is locked based on a count of a plurality of threads that access the cache line and maintained in the cache until all of the plurality of threads have loaded the cache line.

Abstract: A method, apparatus and computer program product for responding to an indirect utterance in a dialogue between a user and a conversational system is described. An indirect utterance is received. A parse structure of the indirect utterance is generated. The indirect utterance is an utterance which does not match a user goal expressed as elements of a knowledge graph. The parse structure is connected through the knowledge graph to a user goal to issue a user request which is not stated in the indirect utterance. The parse structure is connected using a matching process which matches the parse structure with the connected user goal in the knowledge graph according to a similarity of the parse structure and a portion of the knowledge graph including the connected user goal. A system response is performed based on the connected user goal.

Abstract: A method for parallelization of a numeric optimizer includes detecting an initialization of a numeric optimization process of a given function. The method computes a vector-distance between an input vector and a first neighbor vector of a set of neighbor vectors. The method predicts, using the computed vector-distance, a subset of the set of neighbor vectors. The method pre-computes, in a parallel processing system, a set of evaluation values in parallel, each evaluation value corresponding to one of the subset of the set of neighbor vectors. The method detects a computation request from the numeric optimization process, the computation request involving at least one of the set of evaluation values. The method supplies, in response to receiving the computation request, and without performing a computation of the computation request, a parallelly pre-computed evaluation value from the set of evaluation values to the numeric optimization process.

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: A method for adaptive error correction in quantum computing includes executing a calibration operation on a set of qubits, the calibration operation determining an initial state of a quantum processor. In an embodiment, the method includes estimating, responsive to determining an initial state of the quantum processor, a runtime duration for a quantum circuit design corresponding to a quantum algorithm, the quantum processor configured to execute the quantum circuit design. In an embodiment, the method includes computing an error scenario for the quantum circuit design. In an embodiment, the method includes selecting, using the error scenario and the initial state of the quantum processor, a quantum error correction approach for the quantum circuit design. In an embodiment, the method includes transforming the quantum algorithm into the quantum circuit design, the quantum circuit design including a set of quantum logic gates.

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.