A system and method are provided to enable non-quantum experts to schematically represent, simulate and quantify the performance of physically realistic photonic quantum circuits. The framework offers the flexibility for users—not necessarily familiar with the fundamentals of quantum mechanics—to create circuits and work with simple inputs and outputs, while the complexities of manipulating high dimensionality quantum Hilbert spaces supporting photonic and physical quantum object states are handled with the use of purpose-built tools. The tools include a user-friendly method for defining classical photonic circuits which may be coupled to physical objects such as qubits, quantum input states, as well as classical and quantum measurement devices. The tools feature classical-to-quantum S-matrix conversion, quantum S-matrix extraction, as well as capabilities for defining and extracting quantum error parameters. The framework also supports extraction of post-measurement quantum states for use in subsequent circuits or simulators.

An anomaly detection device according to the embodiment includes a prediction unit and an anomaly score calculation unit. The prediction unit performs a process to obtain, at each time step of the time series data of m dimensions, distribution parameters required to express a continuous probability distribution representing a distribution state of predicted values that can be obtained at a time step t of the time series data of m dimensions. The anomaly score calculation unit performs a process to calculate, using distribution parameters obtained by the prediction unit, an anomaly score corresponding to an evaluation value representing evaluation of a magnitude of anomaly in an actual measurement value at the time step t of time series data of m dimensions.

One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.

A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided.

According to an embodiment of the disclosure, an electronic device includes a processor and a memory operationally connected to the processor and configured to store instructions that, when executed by the processor, cause the processor to configure a time period comprising multiple unit durations, check for utilization of the processor for each of the multiple unit durations of the time period, collect at least one variation of the utilization of the processor based on the utilization of the processor for each of the multiple unit durations, acquire a temporal probability density function based on the at least one collected variation, determine a probability density function corresponding to the temporal probability density function based on a previously stored probability density function table, and determine an operating frequency for a next unit duration based on at least part of the identified probability density function. Various other embodiments are possible.

Techniques and systems for generating constrained random stimuli during functional verification of a design under verification (DUV) are described. Some embodiments can compute an observed probability distribution for each variable in a set of variables based on at least a first random solution generated using a set of constraints that are defined over the set of variables. The embodiments can then compute a correction probability distribution for each variable in the set of variables based on the observed probability distribution and an intended probability distribution. Next, while generating at least a second random solution using the set of constraints, the embodiments can select a random value for a given variable in the set of variables based on the correction probability distribution for the given variable. The observed probability distribution can be continuously updated and stored as constrained random stimuli are generated.

One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.

A computer-implemented method for inferring a device feature of a device produced on a wafer. The method includes: providing a wafer feature model associating a wafer position indicating a position of a produced device on the wafer to a device feature, wherein the wafer feature model is configured to be trained by one or more wafer feature maps and particularly configured as a Gaussian process model, providing a sample device feature of at least one device at a sample wafer position, and inferring the device feature of at least one other device of the wafer depending on the provided wafer feature model.

A computer implemented method for developing a probabilistic graphical representation, the probabilistic graphical representation comprising nodes and links between the nodes indicating a relationship between the nodes, wherein the nodes represent conditions, the method comprising: using a language model to produce a context aware embedding for said condition; enhancing said embedding with one or more features to produce an enhanced embedded vector; and using a machine learning model to map said enhanced embedded vector to a value, wherein said value is related to the node representing said condition or a neighbouring node, wherein said machine learning model has been trained using said enhanced embedded vectors and observed values corresponding to said enhanced embedded vectors.

A method for optimal design of experiments for joint model selection and parametrization determination of a symbolic mathematical model includes: determining a prediction value for a given inquiry data point, functional form and parameterization for conducting an experiment relating to a system under investigation; assuming a set of input-output data pairs as a starting point in a model discovery process relating to the system under investigation; performing discovery of symbolic models minimizing complexity for a bounded misfit, or minimizing a misfit measure, subject to bounded complexity; determining a new data point through optimal experimental design that informs best as for the underlying symbolic models; and updating a posterior distribution, given results of the experiment relating to the system under investigation for the determined new data point to enable informed assessment among a plurality of functional forms and parameterizations. An apparatus configured to perform the method is also provided.