Red Hat

A computer-implemented method can include: capturing parameter data descriptive of values of parameters of a machine-learned model, the parameter data comprising a plurality of parameter lists respectively associated with a plurality of nodes of the machine-learned model, wherein the parameter data is captured during a forward-pass operation of the machine-learned model; sorting the parameter data to generate a sorted set of parameter lists; generating a plurality of parameter checksums, each checksum corresponding to one of the parameter lists of the sorted set of parameter lists; generating a model checksum of the plurality of parameter checksums; and authenticating the machine-learned model based on the model checksum.

Techniques for analyzing a response generated by an ML model and determining an appropriate virtualization construct to embed the response into for secure transmission of the response to a requesting entity are disclosed. In response to receiving a prompt from a requesting entity, a machine learning (ML) model may generate a response to the prompt. The prompt may be analyzed to determine a virtualization construct to embed the response into for secure transmission of the response to the requesting entity. A set of rules for determining a virtualization construct may be used to analyze response characteristics and requesting entity information to determine the virtualization construct to embed the response into. Once the appropriate virtualization construct has been determined, the response may be embedded in the appropriate virtualization construct. The appropriate virtualization construct may be provided to requesting entity.

Aspects of the present disclosure relate to insertion of guardian layers in container images for secure layer importing. More specifically, a method of the present disclosure includes receiving a set of instructions to build a container image comprising a plurality of layers. The method includes detecting a new layer from the plurality of layers within the set of instructions. The method includes inserting a guardian layer between the new layer and at least one of the plurality of layers. The method includes building the container image based on the set of instructions.

A system and method of an anomaly detection and correction system for benevolent adversarial attacks. The method includes receiving a request to provide an input dataset to a machine learning (ML) model, trained using a training data distribution, to generate predictions based on input data. The method includes calculating, based on model traits of the model and the input dataset, an anomaly score indicating an accuracy of the model associated with generating a prediction output. The method includes adjusting, by a processing device based on the anomaly score, the input dataset to generate a modified input dataset. The method includes increasing a likelihood of an accurate prediction by the ML model when generating a prediction output by providing the modified input dataset to the model instead of the input dataset.

A quantum computing system receives a first qubit reservation request from a first quantum service, the first qubit reservation request identifying a first quantity of qubits. A first set of qubits equal in number to the first quantity of qubits is determined based on a qubit data structure that identifies a first plurality of qubits. The qubit data structure is altered to reserve the first set of qubits for the first quantum service. Qubit reservation information that identifies the first set of qubits is sent to the first quantum service.

A service executing on a first quantum computing system (QCS) receives an indication that a process executing on the first QCS desires to communicate with a second QCS. The service accesses QCS type information that identifies the second QCS as a first QCS type of a plurality of different QCS types. The service initiates a first external QCS agent on the first QCS that is operable to communicate with the second QCS based on a first QCS configuration of a plurality of QCS configurations, the first QCS configuration corresponding to the first QCS type.

A virtual machine including a security module is initialized, wherein the virtual machine utilizes a boot process configuration that is to be utilized by a computing device. The computing device accesses a configuration value derived by the security module within the virtual machine. The configuration value is associated with a decryption key that is operable to decrypt data stored on a storage device. A boot process action of the computing device is implemented based on the configuration value.

A computing system obtains a set of instructions configured to execute on a classical computing system. The set of instructions are evaluated to identify a cryptographic algorithm having a cryptographic algorithm type implemented by the set of instructions. Based on the cryptographic algorithm type, a candidate quantum algorithm comprising quantum instructions operable to attempt to breach the cryptographic algorithm is selected from a plurality of quantum algorithms. The set of instructions are translated into cryptographic quantum instructions that implement the cryptographic algorithm. Based on the cryptographic quantum instructions and the quantum instructions, a quantum instruction file is generated that, when executed, attempts to breach the cryptographic algorithm.

A quantum system can obtain a quantum instruction set. The quantum system can classify a quantum algorithm type of the quantum instruction set. The quantum system can evaluate an optimization factor of the quantum instruction set based on the quantum algorithm type. The quantum system can rank a plurality of candidate quantum computing systems based on the optimization factor and metadata associated with the plurality of candidate quantum computing systems. The metadata can be descriptive of performance characteristics of the plurality of candidate quantum computing systems respective to the optimization factor. The quantum system can schedule the quantum instruction set on a selected quantum computing system of the plurality of candidate quantum computing systems based on ranking the plurality of candidate quantum computing systems.

A method, a system, and a non-transitory computer-readable medium are provided. The method includes receiving a data object at a node of a datagrid system. The method includes generating a first association between the data object and a key that identifies the node in the datagrid system. The method includes storing the first association in a first cache of the node. The method includes converting the data object to an embedding object. The method includes generating a second association between the embedding object and the key. The method includes storing the second association in a second cache of the node.