Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a bill of materials (BOM) optimization problem, the BOM optimization problem comprising a task of determining a BOM recommendation that satisfies one or more constraints, wherein each constraint comprises one or more constraint variables and a constraint constant; generating, based on the data representing the BOM optimization problem, a quadratic unconstrained binary optimization (QUBO) formulation, wherein the QUBO formulation comprises i) for each constraint variable in the one or more constraints, a binary representation of the constraint variable and ii) a first parameter that depends on a difference between a respective constraint constant and the binary representations of the constraint variables; and obtaining data representing a solution to the BOM optimization problem from a quantum computing resource.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a bill of materials (BOM) optimization problem, the BOM optimization problem comprising a task of determining a BOM recommendation that satisfies one or more constraints, wherein each constraint comprises one or more constraint variables and a constraint constant; generating, based on the data representing the BOM optimization problem, a quadratic unconstrained binary optimization (QUBO) formulation, wherein the QUBO formulation comprises i) for each constraint variable in the one or more constraints, a binary representation of the constraint variable and ii) a first parameter that depends on a difference between a respective constraint constant and the binary representations of the constraint variables; and obtaining data representing a solution to the BOM optimization problem from a quantum computing resource.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.