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.

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.

Performing quantum file concatenation is disclosed herein. In one example, a quantum file manager receives a request to concatenate a first quantum file comprising a first plurality of qubits and a second quantum file comprising a second plurality of qubits. Responsive to receiving the request, the quantum file manager concatenates the first quantum file and the second quantum file into a concatenated quantum file comprising a third plurality of qubits, wherein the third plurality of qubits comprises a same number of qubits as a union of the first plurality of qubits and the second plurality of qubits, and stores an identical sequence of data values as the first plurality of qubits followed by the second plurality of qubits.

One example method includes dynamically selecting a quantum processing unit. During execution of a hybrid application, a quantum execution bundle is processed to identify characteristics that are used to select an optimal quantum processing unit. For each iteration, the optimal quantum processing unit can be dynamically selected and inserted into the execution.

One example method includes dynamically selecting a quantum processing unit. During execution of a hybrid application, a quantum execution bundle is processed to identify characteristics that are used to select an optimal quantum processing unit. For each iteration, the optimal quantum processing unit can be dynamically selected and inserted into the execution.

Systems and methods involving quantum machines, hybrid quantum machines, aspects of quantum information technology and/or other features are disclosed. In one exemplary implementation, a system is provided comprising a quantum register that stores quantum information using qubits, wherein the qubits are configured to store the quantum information using particles or objects arranged in a lattice of quantum gates, a clock that provides a clock cycle to the quantum register, and a qubit-tie computing component coupled to the quantum register, wherein the qubit-tie computing component is configured to shift the quantum information between the qubits, wherein the system stores the qubits in different states using physical qualities, which may define qubits that are configured to be entangled and superposed at a same time. Further, the quantum register may comprise an entanglement component, and/or the qubit-tie computing component may comprise a superposition component.

Methods, systems, and apparatus for solving computational tasks using quantum computing resources. In one aspect a method includes receiving, at a quantum formulation solver, data representing a computational task to be performed; deriving, by the quantum formulation solver, a formulation of the data representing the computational task that is formulated for a selected type of quantum computing resource; routing, by the quantum formulation solver, the formulation of the data representing the computational task to a quantum computing resource of the selected type to obtain data representing a solution to the computational task; generating, at the quantum formulation solver, output data including data representing a solution to the computational task; and receiving, at a broker, the output data and generating one or more actions to be taken based on the output data.

Embodiments are disclosed for a method for a security model. The method includes generating a machine learning model that determines probabilities of a plurality of specific techniques, tactics, and procedures (TTPs) for a security domain. The method also includes generating a machine learning model that maps multiple TTPs to a polytope for the security domain. Additionally, the method includes generating a polytope visualization having multiple visualized points in a multi-dimensional space. The visualized points represent corresponding TTPs of a same type and associated probabilities. Further, a disposition of each of the visualized points is based on the determined probabilities and the mapped plurality of TTPs.

Embodiments are disclosed for a method for a security model. The method includes generating a machine learning model that determines probabilities of a plurality of specific techniques, tactics, and procedures (TTPs) for a security domain. The method also includes generating a machine learning model that maps multiple TTPs to a polytope for the security domain. Additionally, the method includes generating a polytope visualization having multiple visualized points in a multi-dimensional space. The visualized points represent corresponding TTPs of a same type and associated probabilities. Further, a disposition of each of the visualized points is based on the determined probabilities and the mapped plurality of TTPs.