Abstract: Techniques and a system for quantum circuit decomposition by integer programming are provided. In one example, a system includes a quantum circuit decomposition component and a simulation component. The quantum circuit decomposition component generates graphical data for a quantum circuit that is indicative of a graphical representation of the quantum circuit. The graphical representation is formatted as a hypergraph. The simulation component simulates the quantum circuit based on the graphical data associated with the hypergraph.

Abstract: A processor may include a set of primitive operators, receive a set of data-driven operators, at least one of the set of data-driven operators including a machine learning model, and receive an input-output data pair set. Based on a grammar specifying rules for linking the set of primitive operators and the set of data-driven operators, the processor may search among the set of primitive operators and the set of data-driven operators to find a symbolic model that fits the input-output data set.

Abstract: A hybrid classical-quantum computing device to execute a quantum circuit corresponding to a variational problem, is configured. The configuring further comprises causing the hybrid classical-quantum computing device to execute the quantum circuit by performing an adiabatic progression operation, wherein the adiabatic progression operation comprises increasing the difficulty of the variational problem from a simplified version of the problem to the variational problem.

Abstract: A hybrid classical-quantum computing device to execute a quantum circuit corresponding to a variational problem, is configured. The configuring further comprises causing the hybrid classical-quantum computing device to execute the quantum circuit by performing an adiabatic progression operation, wherein the adiabatic progression operation comprises increasing the difficulty of the variational problem from a simplified version of the problem to the variational problem.

Abstract: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

Abstract: A method includes receiving, by a controller, a numerical coefficient to decompose into at least one mathematical expression. The method also includes decomposing, by the controller, the numerical coefficient into the at least one mathematical expression. Decomposing takes into account a complexity cost of the at least one mathematical expression. The method also includes generating an output data that comprises the at least one mathematical expression.

Abstract: A hybrid classical-quantum computing device to execute a quantum circuit corresponding to a variational problem, is configured. The configuring further comprises causing the hybrid classical-quantum computing device to execute the quantum circuit by performing an adiabatic progression operation, wherein the adiabatic progression operation comprises increasing the difficulty of the variational problem from a simplified version of the problem to the variational problem.

Abstract: A hybrid classical-quantum computing device to execute a quantum circuit corresponding to a variational problem, is configured. The configuring further comprises causing the hybrid classical-quantum computing device to execute the quantum circuit by performing an adiabatic progression operation, wherein the adiabatic progression operation comprises increasing the difficulty of the variational problem from a simplified version of the problem to the variational problem.

Abstract: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

Abstract: Techniques and a system for quantum circuit decomposition by integer programming are provided. In one example, a system includes a quantum circuit decomposition component and a simulation component. The quantum circuit decomposition component generates graphical data for a quantum circuit that is indicative of a graphical representation of the quantum circuit. The graphical representation is formatted as a hypergraph. The simulation component simulates the quantum circuit based on the graphical data associated with the hypergraph.

Abstract: Described herein is a simulation of an input quantum circuit, comprising a machine-readable specification of a quantum circuit. Aspects include partitioning the input quantum circuit into a group of sub-circuits based on at least two groups of qubits identified for tensor slicing, wherein the resulting sub-circuits have associated sets of qubits to be used for tensor slicing. The simulating can occur in stages, one stage per sub-circuit. A set of qubits associated with a sub-circuit can be used to partition the simulated quantum state tensor for the input quantum state circuit into quantum state tensor slices, and the quantum gates in that sub-circuit can used to update the quantum state tensor slices into updated quantum state tensor slices. The updated quantum state tensor slices are stored to secondary storage as micro slices.

Abstract: A processor may include a set of primitive operators, receive a set of data-driven operators, at least one of the set of data-driven operators including a machine learning model, and receive an input-output data pair set. Based on a grammar specifying rules for linking the set of primitive operators and the set of data-driven operators, the processor may search among the set of primitive operators and the set of data-driven operators to find a symbolic model that fits the input-output data set.

Abstract: A dimensionality analysis method, system, and computer program product, include conducting a search that determines which valid expressions in a data set satisfies a defined free-form grammar that describes the data set.

Abstract: Techniques and a system to facilitate quantum computation of Monte Carlo minimization are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor can perform an Nth order moment expectation computation process to compute an expected value of a quantum state associated with a quantum circuit description. The classical processor can execute computer executable components stored in a memory, where the computer executable components comprise a variational optimization component. The variational optimization component can perform an optimization process associated with a Monte Carlo minimization process to iteratively determine a variational parameterization for an Nth expectation value and associated trial state based on samples of the Nth order moment expectation computation process.

Abstract: Described herein is a simulation of an input quantum circuit, comprising a machine-readable specification of a quantum circuit. Aspects include partitioning the input quantum circuit into a group of sub-circuits based on at least two groups of qubits identified for tensor slicing, wherein the resulting sub-circuits have associated sets of qubits to be used for tensor slicing. The simulating can occur in stages, one stage per sub-circuit. A set of qubits associated with a sub-circuit can be used to partition the simulated quantum state tensor for the input quantum state circuit into quantum state tensor slices, and the quantum gates in that sub-circuit can used to update the quantum state tensor slices into updated quantum state tensor slices. The updated quantum state tensor slices are stored to secondary storage as micro slices.

Abstract: Techniques and a system for quantum circuit decomposition by integer programming are provided. In one example, a system includes a quantum circuit decomposition component and a simulation component. The quantum circuit decomposition component generates graphical data for a quantum circuit that is indicative of a graphical representation of the quantum circuit. The graphical representation is formatted as a hypergraph. The simulation component simulates the quantum circuit based on the graphical data associated with the hypergraph.

Abstract: A method includes receiving, by a controller, a set of data. The set of data includes data of at least one explanatory variable and data of at least one response variable. The method also includes receiving, by the controller, an input that identifies a relevant explanatory variable. The method also includes generating, by the controller, an output data that corresponds to a symbolic expression that represents a relationship between the at least one explanatory variable and the at least one response variable. The symbolic expression is responsive to the identified relevant explanatory variable, and the symbolic expression includes a mathematically stable expression.

Abstract: Free-form discovery of differential equations for modeling a physical system or process under investigation that includes defining a formal language sentence grammar that describes admissible relationships between quantities, and incorporating the use of such grammar for free-(functional) form, automatic discovery of differential equations. The method requires minimum knowledge of the desired differential equation and is universally applicable to ordinary and partial differential equations.

Abstract: Techniques and a system to facilitate meta-level quantum computation are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor can perform an expectation computation process to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description. The classical processor can execute computer executable components stored in a memory, where the computer executable components comprise a meta-level variational optimization component. The meta-level variational optimization component can perform a meta-level optimization process associated with a k-eigenvalue decomposition process to iteratively determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process.

Abstract: Solving mixed integer problems using a hybrid classical-quantum computing system includes generating a plurality of decision variables for a function associated with a combinatorial optimization problem by a first processor using an optimizer, and deriving at least one quantum state parameter for a quantum processor based upon one or more of the decision variables. The quantum processor is initiated in a quantum state based upon the at least one quantum state parameter. A plurality of intermediate quantum states of the quantum processor are measured using a plurality of quantum measurements of the quantum state to obtain a plurality of samples. The plurality of samples are evaluated by the first processor to obtain a measure of a quality of the quantum state and of one or more solutions to the combinatorial optimization problem.