Quantum and digital processor hybrid systems and methods to solve problems

Abstract

Quantum processors and classical computers are employed together to solve computational problems. The classical computer may include a parameter learning module that produces a set of parameters. The quantum processor may be configured with the set of parameters to define a problem Hamiltonian and operated to perform adiabatic quantum computation and/or quantum annealing on the problem Hamiltonian to return a first solution to the problem. The parameter learning module of the classical computer may then be used to revise the set of parameters by performing a classical optimization, such as a classical heuristic optimization. The quantum processor may then be programmed with the revised set of parameters to return a revised solution to the problem. The quantum processor may include a superconducting quantum processor implementing superconducting flux qubits.

1 . A quantum processor and classical computer based method of using both a quantum processor and a classical computer to solve a problem, wherein the quantum processor and the classical computer are communicatively coupled to one another, the method comprising:
 generating an initial set of parameters for the quantum processor via the classical computer;   providing the initial set of parameters from the classical computer to the quantum processor via a communicative coupling between the classical computer and the quantum processor;   determining a first solution to the problem via a quantum computation performed by the quantum processor using the initial set of parameters;   varying at least one parameter of the initial set of parameters via the classical computer to generate a revised set of parameters for the quantum processor, wherein varying at least one parameter of the initial set of parameters includes performing a classical optimization via the classical computer;   providing the revised set of parameters from the classical computer to the quantum processor via the communicative coupling between the classical computer and the quantum processor; and   determining a revised solution to the problem via a quantum computation performed by the quantum processor using the revised set of parameters.   
     
     
         2 . The method of  claim 1  wherein determining a first solution to the problem via a quantum computation performed by the quantum processor using the initial set of parameters includes determining a first solution to the problem via an adiabatic quantum computation performed by the quantum processor using the initial set of parameters, and wherein determining a revised solution to the problem via a quantum computation performed by the quantum processor using the revised set of parameters includes determining a revised solution to the problem via an adiabatic quantum computation performed by the quantum processor using the revised set of parameters. 
     
     
         3 . The method of  claim 1  wherein determining a first solution to the problem via a quantum computation performed by the quantum processor using the initial set of parameters includes determining a first solution to the problem via an implementation of quantum annealing performed by the quantum processor using the initial set of parameters, and wherein determining a revised solution to the problem via a quantum computation performed by the quantum processor using the revised set of parameters includes determining a revised solution to the problem via an implementation of quantum annealing performed by the quantum processor using the revised set of parameters. 
     
     
         4 . The method of  claim 1  wherein performing a classical optimization via the classical computer includes performing a classical heuristic optimization via the classical computer. 
     
     
         5 . The method of  claim 4  wherein performing a classical heuristic optimization via the classical computer includes refining the first solution to the problem via a classical heuristic optimization algorithm performed by the classical computer. 
     
     
         6 . The method of  claim 4  wherein the classical heuristic optimization algorithm includes at least one of: local search, tabu search, a genetic algorithm, or simulated annealing. 
     
     
         7 . The method of  claim 1  wherein the first solution to the problem is an approximate solution and determining a revised solution to the problem includes determining a second solution to the problem that is at least as good as the first solution to the problem. 
     
     
         8 . The method of  claim 1  wherein the quantum processor includes a plurality of qubits and a plurality of coupling devices, and wherein the set of initial parameters includes parameters that control the plurality of qubits and parameters that control the plurality of coupling devices. 
     
     
         9 . The method of  claim 8  wherein a Hamiltonian of the quantum processor is given by: 
       
         
           
             
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         and wherein generating an initial set of parameters for the quantum processor via the classical computer includes generating initial values for the h i  terms and the J ij  terms in the Hamiltonian of the quantum processor. 
       
     
     
         10 . The method of  claim 9  wherein varying at least one parameter of the initial set of parameters includes varying at least one of: an h i  term in the Hamiltonian of the quantum processor or a J ij  term in the Hamiltonian of the quantum processor. 
     
     
         11 . The method of  claim 1 , further comprising:
 comparing the revised solution to the first solution via the classical computer.   
     
     
         12 . The method of  claim 1  wherein the classical computer includes a system memory storing a parameter learning module and wherein varying at least one parameter of the initial set of parameters via the classical computer includes varying at least one parameter of the initial set of parameters via the parameter learning module of the classical computer. 
     
     
         13 . The method of  claim 12 , further comprising:
 comparing the revised solution to the first solution via the classical computer;   determining, via the parameter learning module, that varying a first parameter in a first direction leads to an improved revised solution compared to the first solution; and   varying the first parameter in the first direction via the parameter learning module.   
     
     
         14 . A computing system operable to solve problems, the computing system comprising:
 a quantum processor;   a classical computing subsystem communicatively coupled to the quantum processor, the classical computing subsystem including a classical processor and a system memory storing a parameter learning module, wherein the parameter learning module is configured to generate a set of parameters for the quantum processor and to revise the set of parameters for the quantum processor to produce a revised set of parameters by varying at least one parameter of the set of parameters, wherein varying at least one parameter of the set of parameters includes performing a classical optimization.   
     
     
         15 . The system of  claim 14  wherein a Hamiltonian of the quantum processor is given by: 
       
         
           
             
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         and wherein the parameter learning module is configured to generate values for the h i  terms and the J ij  terms in the Hamiltonian of the quantum processor. 
       
     
     
         16 . The system of  claim 14  wherein the quantum processor is operable to perform at least one of: adiabatic quantum computation or quantum annealing. 
     
     
         17 . The system of  claim 14 , further comprising a solver module stored in the system memory of the classical computing subsystem, the solver module configured to:
 receive the set of parameters from the parameter learning system and employ the set of parameters to control the quantum processor and cause the quantum processor to provide a first solution to a problem; and   receive the revised set of parameters from the parameter learning system and employ the revised set of parameters to control the quantum processor and cause the quantum processor to provide a revised solution to the problem.   
     
     
         18 . The system of  claim 14  wherein the parameter learning module is configured to vary at least one parameter of the set of parameters by performing a classical heuristic optimization. 
     
     
         19 . The system of  claim 18  wherein the parameter learning module is configured to vary at least one parameter of the set of parameters by performing at least one of: local search, tabu search, a genetic algorithm, or simulated annealing. 
     
     
         20 . The system of  claim 14  wherein the quantum processor comprises a superconducting quantum processor implementing superconducting flux qubits.