Mapping of logical qubits to physical qubits is provided. In various embodiments, a first candidate subgraph is selected from a hardware graph. The hardware graph represents a physical quantum circuit. The hardware graph comprises a plurality of nodes corresponding to physical qubits and a plurality of edges corresponding to coupling among the plurality of qubits. An accepted subgraph is determined by: setting the accepted subgraph to be the first candidate subgraph; mapping a quantum circuit comprising a plurality of logical qubits to the accepted subgraph; generating a second candidate subgraph of the hardware graph based on the accepted subgraph; mapping the quantum circuit to the second candidate subgraph; comparing fidelities of the accepted subgraph and the second candidate subgraph for the quantum circuit; if the fidelity of the second candidate subgraph is greater than the fidelity of the accepted subgraph, setting the accepted subgraph to be the second candidate subgraph; if the fidelity of the second candidate subgraph is less than or equal to the fidelity of the accepted subgraph, setting the accepted subgraph to be the second candidate subgraph with a time-dependent probability.

A discrete geometrical pattern library guides a method for design optimization of a finite element model in a computer aided design (CAD) environment. Boundary conditions are applied to the finite element model, design variables for the bounded finite element model are initialized, and an objective function for the finite element model is evaluated. A gradient of the objective function is evaluated with respect to the design variables, an appearance constraint function is evaluated for the finite element model, and a gradient of the appearance constraint function is evaluated with respect to the design variables. The design variables are updated using a mathematical programming, and a convergence in the design optimization is detected, producing a converged design optimization of the finite element model is produced.

The present disclosure relates to a machine learning-based automatic routing method and apparatus for semiconductor equipment, and the machine learning-based automatic routing method for semiconductor equipment according to one embodiment of the present disclosure includes: a first operation of disposing semiconductor equipment and ancillary equipment; a second operation of recognizing connection points (points of connection, POC) which are three-dimensional (3D) coordinates of the semiconductor equipment and the ancillary equipment; and a third operation of generating an optimal path which connects the connection points (POC) using a machine learning algorithm.

The present disclosure relates to multi-objective optimization of complex technical systems. A method of designing a Pareto-optimal layout of a system includes processing input data relating to defining a layout space of layout parameters, a target space of target parameters, and constraints in the layout parameters and the target parameters, determining one or more sets of layout parameter values, each set specifying a layout configuration of the system to be modeled, receiving objective responses for the system having layout configurations specified by the one or more sets of layout parameter values, wherein each objective response corresponds to a target parameter value achieved by the system when having a layout configuration specified by one of the sets of layout parameter values, applying multi-objective optimization with respect to the target parameters to determine one or more further sets of layout parameter values, receiving objective responses for the system having layout configurations specified by the one or more further sets of layout parameter values, repeating the steps of applying multi-objective optimization and receiving respective objective responses until an abort criterion is met, and providing a user interface for Pareto-optimal design of the system to be modeled, the user interface configured for selecting a specific layout configuration on basis of visualizing objective trade-offs inferred from the objective responses for the respective sets of layout parameter values, wherein the trade-offs are inferred from the objective responses based on determining Pareto-optimal point.

Systems and methods automatically construct a realization of a model from an available set of alternative co-simulation components, where the realization meets one or more objectives, such as fidelity, execution speed, or memory usage, among others. The systems and methods may construct the realization model by setting up and solving a constrained optimization problem, which may select particular ones of the alternative co-simulation components to meet the objectives. The systems and methods may configure the realization, and execute the realized model through co-simulation. The systems and methods may employ and manage different execution engines and/or different solvers to run the realization of the model.

An object of the present invention is to provide a material design system and a material design method, which each reduce computational complexity and enable efficient material design. A material design system according to the present invention, which designs a material that can achieve a desired material function, includes an input device receiving the desired material function, and an arithmetic unit calculating a constitutional material. The arithmetic unit includes a structure calculation part that calculates a molecular characteristic amount meeting the desired material function using a method that can express molecular characteristic amount exhibited by a set of two or more atoms or molecules, and a molecular calculation part that calculates the constituent material, which achieves the molecular characteristic amount calculated by the structure calculation part, using a method that can express a molecule itself.

A differentiable simulator for simulating the cutting of soft materials by a cutting instrument is provided. In accordance with one aspect of the disclosure, a method for simulating a cutting operation includes: receiving a mesh for an object, modifying the mesh to add virtual nodes associated with a predefined cutting plane, optimizing a set of parameters associated with a simulator based on ground-truth data, and running a simulation via the simulator to generate outputs that include trajectories associated with a cutting instrument. Optimizing the set of parameters can include performing inference based on a set of ground-truth trajectories captured using sensors to measure real-world cutting operations. The inference techniques can employ stochastic gradient descent, stochastic gradient Langevin dynamics, or a Bayesian approach. In an embodiment, the simulator can be utilized to generate control signals for a robot based on the simulated trajectories.

A method and system including identifying, via at least one processor, a user role; configuring, via the at least one processor, an interface for the user role, wherein configuring the interface includes determining an interface view for the user role; and displaying, via the at least one processor, the interface with the interface view.

The present disclosure is directed to a method and system for hierarchical multi-scale design with the aid of a digital computer. A hierarchical representation of a shape and material distribution is constructed which satisfies a top-level constraint at a top-level of representation. Properties for families of designs at each of the lower levels of representation that satisfy additional constraints link each of the lower levels of representation to at least a next higher level of the representation.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using data format conversion (e.g., of output(s) from generative design processes) and user interface techniques that facilitate the production of 3D models of physical structures that are readily usable with 2.5-axis subtractive manufacturing, include: modifying smooth curves, which have been fit to contours representing discrete height layers of an object, to facilitate the 2.5-axis subtractive manufacturing; preparing an editable model of the object using a parametric feature history, which includes a sketch feature, to combine extruded versions of the smooth curves to form a 3D model of the object in a boundary representation format; reshaping a subset of the smooth curves responsive to user input with respect to the sketch feature; and replaying the parametric feature history to reconstruct the 3D model of the object, as changed by the user input.