Systems and methods for interacting with a quantum computing system
View Patent ↗Systems and methods that employ interactions between quantum computing systems and digital computing systems are described. For an iterative method, a quantum computing system may be designed, operated, and/or adapted to provide a rate of convergence that is greater than the rate of convergence of a digital supercomputer. When the digital supercomputer is iteratively used to evaluate an objective function at a cost incurred of C per iteration, the quantum computing system may be used to provide the input parameter(s) to the objective function and quickly converge on the input parameter(s) that optimize the objective function. Thus, a quantum computing system may be used to minimize the total cost incurred C T for consumption of digital supercomputer resources when a digital supercomputer is iteratively employed to evaluate an objective function.
1. A method of minimizing a total cost incurred for consumption of digital supercomputer resources when a digital supercomputer is employed to optimize a function, wherein the digital supercomputer evaluates the function in response to a call from an external computing system, each call to the digital supercomputer from the external computing system having a cost incurred per call, the method comprising:
initializing communication between the digital supercomputer and the external computing system, wherein the external computing system includes a quantum computing system;
optimizing the function, wherein optimizing the function comprises, for a plurality of iterations:
generating a candidate value for at least one input parameter by the quantum computing system,
transmitting, in a respective call, the candidate value for at least one input parameter from the quantum computing system to the digital supercomputer, and
evaluating the function based on the candidate value for at least one input parameter by the digital supercomputer,
until a value for at least one input parameter that at least approximately optimizes the function is found, wherein the number of iterations in the plurality of iterations is dependent on a rate of convergence of the quantum computing system, and wherein the rate of convergence of the quantum computing system is greater than a rate of convergence of the digital supercomputer such that the number of iterations is minimized, and wherein the total cost incurred is minimized; and
in response to finding the value for at least one input parameter that at least approximately optimizes the function:
stopping the iteration, and
returning the found value for at least one input parameter that at least approximately optimizes the function by the digital supercomputer.
2. The method of claim 1 wherein generating a candidate value for at least one input parameter by the quantum computing system includes performing at least one of adiabatic quantum computation and quantum annealing by the quantum computing system.
3. The method of claim 1 wherein transmitting the candidate value for at least one input parameter from the quantum computing system to the digital supercomputer includes transmitting the candidate value for at least one input parameter from the quantum computing system to the digital supercomputer by at least one of: a direct electrical connection, a wireless connection, a remote connection, or an Internet connection.
4. The method of claim 1 wherein the number of iterations in the plurality iterations is greater than one, and optimizing the function further comprises, for at least one iteration:
providing feedback to the external computing system from the digital supercomputer based on evaluating the function based on the candidate value for at least one input parameter by the digital supercomputer, wherein feedback from an current one of the iterations influences generating a candidate value for at least one input parameter by the quantum computing system in a next one of the iterations.
5. The method of claim 4 wherein the number of iterations in plurality iterations is upper bounded by a value selected from a group consisting of: 1000, 100, and 10.
6. The method of claim 4 wherein generating a candidate value for at least one input parameter by the quantum computing system includes generating multiple candidate values for the at least one input parameter by the quantum computing system, and further comprising, for each iteration of the plurality of iterations:
selecting a candidate value for the at least one input parameter from the multiple candidate values for the at least one input parameter by the external computing system.
7. The method of claim 6 wherein selecting a candidate value for the at least one input parameter from the multiple candidate values for the at least one input parameter by the external computing system includes selecting a candidate value for the at least one input parameter that occurs most frequently among the multiple candidate values for the at least one input parameter by the external computing system.
8. The method of claim 1 wherein generating a candidate value for at least one input parameter by the quantum computing system includes generating respective candidate values for multiple input parameters by the quantum computing system.
9. A hybrid computing system that optimizes a function based on at least one input parameter, the hybrid computing system comprising:
a quantum computing system that generates candidate values for the at least one input parameter, wherein the quantum computing system has a first rate of convergence; and
a digital supercomputer that evaluates the function based on the at least one input parameter, wherein the digital supercomputer has a second rate of convergence, and
wherein for a plurality of iterations:
the quantum computing system generates a candidate value for the at least one input parameter,
the quantum computing system calls the digital supercomputer to transmit the candidate value for the at least one input parameter to the digital supercomputer, and
the digital supercomputer evaluates the function based on the candidate value for the at least one input parameter,
until a value for the at least one input parameter that at least approximately optimizes the function is found, wherein the number of calls to the digital supercomputer from the quantum computing system is dependent on the first rate of convergence of the quantum computing system, and wherein the first rate of convergence of the quantum computing system is greater than the second rate of convergence of the digital supercomputer such that the number of calls to the digital supercomputer from the quantum computing system is minimized; and
in response to finding the value for the at least one input parameter that at least approximately optimizes the function:
the digital supercomputer returns the found value for the at least one input parameter that at least approximately optimizes the function.
10. The hybrid computing system of claim 9 wherein the quantum computing system includes a superconducting quantum processor, and wherein the quantum computing system generates candidate values for the at least one input parameter by at least one of adiabatic quantum computation and quantum annealing.
11. The hybrid computing system of claim 9 wherein the quantum computing system and the digital supercomputer are communicatively coupleable by at least one of a direct electrical connection, a wireless connection, a remote connection, and an Internet connection.
12. A method of operating both a quantum computing system and a digital supercomputer to at least approximately optimize a function based on at least one input parameter, the method comprising:
for a number of iterations:
generating a candidate value for at least one input parameter by the quantum computing system;
programming the digital supercomputer with the candidate value for at least one input parameter; and
evaluating the function based on the candidate value for at least one input parameter by the digital supercomputer,
until a value for at least one input parameter that at least approximately optimizes the function is found, wherein the number of iterations is dependent on a rate of convergence of the quantum computing system, and wherein the rate of convergence of the quantum computing system is greater than a rate of convergence of the digital supercomputer; and
in response to finding a value for at least one input parameter that at least approximately optimizes the function:
stopping the iteration; and
returning the found value for at least one input parameter that at least approximately optimizes the function by the digital supercomputer.
13. The method of claim 12 wherein generating a candidate value for at least one input parameter by the quantum computing system includes performing at least one of adiabatic quantum computation and quantum annealing by the quantum computing system.
14. The method of claim 12 , further comprising:
for at least one iteration:
providing feedback to the quantum computing system from the digital supercomputer based on evaluating the function based on the candidate value for at least one input parameter by the digital supercomputer, wherein feedback from an i th iteration influences generating a candidate value for at least one input parameter by the quantum computing system in an (i+1) th iteration.
15. The method of claim 14 wherein the number of iterations is selected from a group consisting of: a number less than 1000, a number less than 100, and a number less than 10.
16. The method of claim 14 wherein generating a candidate value for at least one input parameter by the quantum computing system includes generating multiple candidate values for the at least one input parameter by the quantum computing system, and further comprising, for each of the number of iterations:
selecting a candidate value for the at least one input parameter from the multiple candidate values for the at least one input parameter by a digital computing system.
17. The method of claim 16 wherein selecting a candidate value for the at least one input parameter from the multiple candidate values for the at least one input parameter by a digital computing system includes selecting a candidate value for the at least one input parameter that occurs most frequently among the multiple candidate values for the at least one input parameter by the digital computing system.
18. The method of claim 12 wherein generating a candidate value for at least one input parameter by the quantum computing system includes generating respective candidate values for multiple input parameters by the quantum computing system.
19. A method of iteratively operating a hybrid computing system to solve a problem, the hybrid computing system comprising a quantum computing system having a first rate of convergence and a digital computing system having a second rate of convergence, wherein the quantum computing system and the digital computing system are in communication with one another and the first rate of convergence of the quantum computing system is greater than the second rate of convergence of the digital computing system, the method comprising:
for a number of iterations:
generating a candidate solution to the problem by the quantum computing system; and
evaluating the candidate solution to the problem by the digital computing system,
until a candidate solution to the problem that satisfies at least one solution criterion is found, wherein the number of iterations is dependent on the first rate of convergence of the quantum computing system; and
in response to finding a candidate solution to the problem that satisfies at least one solution criterion:
stopping the iteration; and
returning the found candidate solution that satisfies at least one solution criterion by the digital computing system.
20. The method of claim 19 wherein generating a candidate solution to the problem by the quantum computing system includes performing at least one of adiabatic quantum computation and quantum annealing by the quantum computing system.
21. The method of claim 19 , further comprising:
for at least one iteration:
providing feedback to the quantum computing system from the digital computing system based on evaluating the candidate solution to the problem by the digital computing system, wherein feedback from an i th iteration influences generating a candidate solution to the problem by the quantum computing system in an (i+1) th iteration.
22. The method of claim 21 wherein the number of iterations is selected from a group consisting of: a number less than 1000, a number less than 100, and a number less than 10.
23. The method of claim 21 wherein generating a candidate solution to the problem by the quantum computing system includes generating multiple candidate solutions to the problem by the quantum computing system, and further comprising, for each of the number of iterations:
selecting a candidate solution to the problem from the multiple candidate solutions to the problem by the digital computing system.
24. The method of claim 23 wherein selecting a candidate solution to the problem from the multiple candidate solutions to the problem by the digital computing system includes selecting a candidate solution to the problem that occurs most frequently among the multiple candidate solutions to the problem by the digital computing system.
25. The method of claim 19 wherein the problem includes optimizing a function based on at least one input parameter and wherein:
generating a candidate solution to the problem by the quantum computing system includes generating a candidate value for at least one input parameter by the quantum computing system; and
evaluating the candidate solution to the problem by the digital computing system includes evaluating the function based on the candidate value for at least one input parameter by the digital computing system.