Placement of bridges connecting CAE tools and virtual ECU simulation tools is facilitated. A virtual developmental environment apparatus includes a processing execution unit and a memory for storing a MILS model including a controller block and a plant block, first setting information, a program for realizing a function in the controller block used in executing simulation of the virtual ECU, and second setting information. The processing execution unit identifies a controller block in the MILS model based on the first setting information, arranges a bridge for connecting the input port and the output port and the I/O port of the virtual ECU to the input port and the output port of the identified controller block, and connects the bridge and the I/O port of the virtual ECU based on the second setting information.

A power intent may be loaded on an integrated circuit (IC) design, where the power intent may be represented by a set of constraints. A logic network may be constructed based on the set of constraints and a rule check which is desired to be performed on the power intent. In response to a failure of the rule check, one or more refutation proofs may be created based on the logic network. A subset of the set of constraints may be identified based on the one or more refutation proofs, where the subset of the set of constraints may include an inconsistency which caused the rule check to fail.

Systems and methods are configured to provide a first problem to be solved to a network of memristors. A second problem to be solved can be gradually provided to the network of memristors. Controlled noise can be applied to the network of memristors for at least a portion of time during which the second problem is “gradually” provided to the network of memristors. A solution to the second problem can be determined.

In a general aspect, a quantum logic circuit is executed on multiple processing nodes in a computing system that includes quantum computing resources. In some aspects, methods of operating the computing system may include obtaining a computer program that includes a quantum logic circuit. The methods may include obtaining hardware resource metadata specifying properties of processing nodes in the computing system. The processing nodes include at least a subset of the quantum computing resources, and the hardware resource metadata includes error rate information and availability information for the respective processing nodes. The methods may include generating execution tasks configured to execute the quantum logic circuit on the processing nodes based on the hardware resource metadata; dispatching the execution tasks to the processing nodes; receiving output data generated by the processing nodes; and producing an output of the computer program based on the output data.

In a general aspect, a quantum logic circuit is executed on multiple processing nodes in a computing system that includes quantum computing resources. In some aspects, methods of operating the computing system may include obtaining a computer program that includes a quantum logic circuit. The methods may include obtaining hardware resource metadata specifying properties of processing nodes in the computing system. The processing nodes include at least a subset of the quantum computing resources, and the hardware resource metadata includes error rate information and availability information for the respective processing nodes. The methods may include generating execution tasks configured to execute the quantum logic circuit on the processing nodes based on the hardware resource metadata; dispatching the execution tasks to the processing nodes; receiving output data generated by the processing nodes; and producing an output of the computer program based on the output data.

A circuit simulation method includes the following: a key character string corresponding to at least one target power supply node is determined; a node identifier corresponding to the at least one target power supply node is searched out from a first netlist corresponding to the to-be-simulated circuit according to the key character string; and a power supply voltage file corresponding to the at least one target power supply node is generated according to the searched-out node identifier, and the to-be-simulated circuit is simulated according to the power supply voltage file. The circuit simulation method and the device provided by the embodiments of the present disclosure may rapidly generate the power supply voltage file corresponding to the target power supply node, which can not only effectively improve the circuit simulation efficiency, but also ensure the accuracy of a simulation result.

A circuit simulation method includes the following: a key character string corresponding to at least one target power supply node is determined; a node identifier corresponding to the at least one target power supply node is searched out from a first netlist corresponding to the to-be-simulated circuit according to the key character string; and a power supply voltage file corresponding to the at least one target power supply node is generated according to the searched-out node identifier, and the to-be-simulated circuit is simulated according to the power supply voltage file. The circuit simulation method and the device provided by the embodiments of the present disclosure may rapidly generate the power supply voltage file corresponding to the target power supply node, which can not only effectively improve the circuit simulation efficiency, but also ensure the accuracy of a simulation result.

Methods, systems and apparatus for simulating quantum circuits including multiple quantum logic gates. In one aspect, a method includes the actions of representing the multiple quantum logic gates as functions of one or more classical Boolean variables that define a undirected graphical model with each classical Boolean variable representing a vertex in the model and each function of respective classical Boolean variables representing a clique between vertices corresponding to the respective classical Boolean variables; representing the probability of obtaining a particular output bit string from the quantum circuit as a first sum of products of the functions; and calculating the probability of obtaining the particular output bit string from the quantum circuit by directly evaluating the sum of products of the functions. The calculated partition function is used to (i) calibrate, (ii) validate, or (iii) benchmark quantum computing hardware implementing a quantum circuit.

Methods, systems and apparatus for simulating quantum circuits including multiple quantum logic gates. In one aspect, a method includes the actions of representing the multiple quantum logic gates as functions of one or more classical Boolean variables that define a undirected graphical model with each classical Boolean variable representing a vertex in the model and each function of respective classical Boolean variables representing a clique between vertices corresponding to the respective classical Boolean variables; representing the probability of obtaining a particular output bit string from the quantum circuit as a first sum of products of the functions; and calculating the probability of obtaining the particular output bit string from the quantum circuit by directly evaluating the sum of products of the functions. The calculated partition function is used to (i) calibrate, (ii) validate, or (iii) benchmark quantum computing hardware implementing a quantum circuit.

A processor utilizes instruction based sampling to generate sampling data sampled on a per instruction basis during execution of an instruction. The sampling data indicates what processor hardware was used due to the execution of the instruction. Software receives the sampling data and generates an estimate of energy used by the instruction based on the sampling data. The sampling data may include microarchitectural events and the energy estimate utilizes a base energy amount corresponding to the instruction executed along with energy amounts corresponding to the microarchitectural events in the sampling data. The sampling data may include switching events associated with hardware blocks that switched due to execution of the instruction and the energy estimate for the instruction is based on the switching events and capacitance estimates associated with the hardware blocks.