In a general aspect, a superconducting quantum circuit system is modeled. In some aspects, a graph representing a quantum circuit system is generated. The graph includes vertices and edges; the edges represent circuit elements of the quantum circuit system, and the vertices represent physical connections between the circuit elements. Inverse inductances, conductances, capacitances, and junction inverse inductances are assigned to respective edges of the graph based on a lumped-element approximation of the quantum circuit system. A coordinate system is determined based on the graph, and a matrix representation of the system is determined based on the coordinate system. A Hamiltonian for the quantum circuit system is determined using the matrix representation, and the quantum circuit system is simulated based on the Hamiltonian.

A method, computer system, and a computer program product for automatic equation transformation from text is provided. The present invention may include receiving a text document. The present invention may then include identifying a mathematical formula expressed in the received text document. The present invention may then include removing a plurality of superfluous language from the received text document based on the identified mathematical formula. The present invention may also include transforming the identified mathematical formula into a symbolic representation based on a trained model. The present invention may finally include outputting the symbolic representation.

A computer-implemented method of extracting properties of one or more variables of a mathematical transformation in a document identified by automatic feature engineering (AFE) is provided. The document is parsed to extract at least one of a mathematical formula including identifying data, a textual description of the one or more variables of the mathematical transformation, or a category identifier to which the document belongs. Constraints are determined to apply to the one or more variables extracted from the mathematical transformations in the document. At least one candidate feature is automatically generated from a portion of an input data that conforms to the determined constraints.

A computer-implemented method of extracting properties of one or more variables of a mathematical transformation in a document identified by automatic feature engineering (AFE) is provided. The document is parsed to extract at least one of a mathematical formula including identifying data, a textual description of the one or more variables of the mathematical transformation, or a category identifier to which the document belongs. Constraints are determined to apply to the one or more variables extracted from the mathematical transformations in the document. At least one candidate feature is automatically generated from a portion of an input data that conforms to the determined constraints.

Provided is an alternative text generating method. The alternative text generating method includes recognizing input visual content, generating input information corresponding to a recognition result of the recognition of the visual content, generating an editing window including an input item to which the input information is automatically input, automatically generating an alternative text, based on an alternative text generation rule and the input information, and displaying the generated alternative text on a text box of the editing window.

Simplification of complex input strings is utilized for user convenience, such that the user may input a complex input string in lieu of performing an external calculation and inputting the result thereof. The simplification identifying at least one operator in the complex input string indicative of a mathematical operation, identifying a first sub-string and a second sub-string in the input string, comparing the first sub-string to an expectation identification associated with the input field to determine a significance of the first sub-string, comparing the first sub-string to a range expectation associated with said significance to determine if the first sub-string meets the range expectation, and calculating a simplified input string by performing the mathematical operation on the first sub-string and the second sub-string.

Simplification of complex input strings is utilized for user convenience, such that the user may input a complex input string in lieu of performing an external calculation and inputting the result thereof. The simplification identifying at least one operator in the complex input string indicative of a mathematical operation, identifying a first sub-string and a second sub-string in the input string, comparing the first sub-string to an expectation identification associated with the input field to determine a significance of the first sub-string, comparing the first sub-string to a range expectation associated with said significance to determine if the first sub-string meets the range expectation, and calculating a simplified input string by performing the mathematical operation on the first sub-string and the second sub-string.

Symbol recognition techniques may be applied to documents comprising various forms of content. Documents including both text and mathematical expressions may be problematic, as applying a recognizer that does not match the content may produce anomalous results. Instead, a parser may evaluate the document to classify respective regions as one of a text region or a mathematics region, based on the characteristics of each type of content. The recognizer corresponding to the content of each region may be applied to produce a composite document comprising both recognized text expressions and recognized mathematical expressions. Additional functionality may be presented based on the recognized content; e.g., text-based tools such as spell-check and natural-language translation may be applied to the text regions that comprise text expressions, and mathematics-based tools such as mathematical equation editors, mathematical expression solvers, and automated assignment grading may be applied to the mathematics regions that comprise mathematical expressions.

A formula system can transform a formula natural language representation (NLR) into a representation which shows the formula in traditional mathematical notation. This transformation can include creating a state machine with transition mappings between states that match to initial parts of the NLR. These transition mappings can include global transition mappings that are first attempted to be matched to the beginning of the NLR and then state specific transition mappings can be matched to the NLR. The formula system can consume the NLR, transitioning from state to state as indicated by the transition mappings and removing the matched initial part from the NLR, until the NLR has been fully consumed. In some cases, the formula system can recursively or iteratively create additional state machines to consume portions of the NLR. Some states provide a result (e.g. portion of a formula representation) which are combined to create the final formula representation.

Devices, systems, and methods can include a source mathematical application, a destination mathematical application, and a mathematical symbol conversion application. Such devices, systems, and methods can, in response to detecting that data indicative of a mathematical symbol has been copied from the source mathematical application, use the mathematical symbol conversion application to identify a first language associated with the source mathematical application and, in response to detecting that the copied data has been pasted into the destination mathematical application, identify a second language associated with the destination mathematical application, convert the copied data from the first language to the second language, and reproduce the copied data in the second language within the second mathematical application, wherein the first and second languages are different from one another, and wherein the reproduced data retains visual and operational qualities of the copied data.