A method for reducing computation time while simulating quantum computation on a classical computer by performing an algorithm used to determine the most efficient input contraction, the method including receiving, by a processor, a tensor network representing a quantum circuit, computing, by the processor, an ordering for the tensor network by an ordering algorithm, contracting, by the processor, the tensor network by eliminating indices according to the ordering resulting in a contracted tensor network, and returning, by the processor, the contracted tensor network.

A method for reducing computation time while simulating quantum computation on a classical computer by performing an algorithm used to determine the most efficient input contraction, the method including receiving, by a processor, a tensor network representing a quantum circuit, computing, by the processor, an ordering for the tensor network by an ordering algorithm, contracting, by the processor, the tensor network by eliminating indices according to the ordering resulting in a contracted tensor network, and returning, by the processor, the contracted tensor network.

Techniques for predicting a landing zone of a wellbore include identifying a first subsurface geological model of a first subterranean layer located under a terranean surface that includes an upper boundary depth of the first subterranean layer and a lower boundary depth of the first subterranean layer; identifying a second subsurface geological model of a second subterranean layer deeper than the first subterranean layer that is independent of the first subsurface geological model and includes an upper boundary depth of the second subterranean layer; correlating a predicted landing zone for a plurality of wellbores using the first and second subsurface geological models that is based on a location of a horizontal portion of each wellbore; and generating data that comprises a representation of the correlated plurality of wellbores for presentation on a graphical user interface (GUI).

Techniques for predicting a landing zone of a wellbore include identifying a first subsurface geological model of a first subterranean layer located under a terranean surface that includes an upper boundary depth of the first subterranean layer and a lower boundary depth of the first subterranean layer; identifying a second subsurface geological model of a second subterranean layer deeper than the first subterranean layer that is independent of the first subsurface geological model and includes an upper boundary depth of the second subterranean layer; correlating a predicted landing zone for a plurality of wellbores using the first and second subsurface geological models that is based on a location of a horizontal portion of each wellbore; and generating data that comprises a representation of the correlated plurality of wellbores for presentation on a graphical user interface (GUI).

A three-dimensional object model is divided into slices that are targeted for an additive manufacturing process operable to deposit material at a variable deposition size ranging between minimum and maximum printable feature sizes. For each of the slices, a thinning algorithm is applied to contours of the slice to form a meso-skeleton. Topological features of the thinned slice are reduced over a number of passes such that a portion of the meso-skeleton is reduced to a single pixel wide line. Based on the number of passes, a slice-specific printable feature size within the range of the minimum and maximum printable feature sizes is determined. An adjusted slice is formed by sweeping the meso-skeleton with the slice-specific printable feature size. The adjusted slices are assembled into an object model which is used to create a manufactured object.

A method including performing a simulation to evaluate a plurality of metrology targets and/or a plurality of metrology recipes used to measure a metrology target, identifying one or more metrology targets and/or metrology recipes from the evaluated plurality of metrology targets and/or metrology recipes, receiving measurement data of the one or more identified metrology targets and/or metrology recipes, and using the measurement data to tune a metrology target parameter or metrology recipe parameter.

A method including performing a simulation to evaluate a plurality of metrology targets and/or a plurality of metrology recipes used to measure a metrology target, identifying one or more metrology targets and/or metrology recipes from the evaluated plurality of metrology targets and/or metrology recipes, receiving measurement data of the one or more identified metrology targets and/or metrology recipes, and using the measurement data to tune a metrology target parameter or metrology recipe parameter.

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.

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.

Implementations described and claimed herein provide systems and methods for developing a reservoir. In one implementation, a static model of the reservoir is received. The static model has one or more clusters of rock types. A reservoir graph is generated from the static model. The reservoir graph represents each of the one or more clusters as a vertex. A graph connectivity of the reservoir graph is defined through a nodal connectivity of neighboring vertices. Pressure values are propagated across three-dimensional space of the reservoir graph using the connectivity. A dynamic model of the reservoir is generated using the pressure values and fluid saturation values.