Quantum circuit optimization routine evaluation and knowledge base generation
Systems, computer-implemented methods, and computer program products to facilitate evaluation of quantum circuit optimization routines and knowledge base generation are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a compilation component that concurrently executes different quantum circuit optimization sequences on multiple copies of a quantum circuit. The computer executable components can further comprise an identification component that identifies at least one of the different quantum circuit optimization sequences that generates an output quantum circuit comprising defined criteria.
1. A system, comprising:
a memory that stores computer executable components; and
at least one processor that executes at least one of the computer executable components that:
executes in parallel, by the at least one processor, different compilers respectively using different quantum circuit optimization sequences on respective identical copies of an input raw quantum circuit to produce respective compiled output quantum circuits for execution on at least one quantum computer, wherein the different quantum circuit optimization sequences comprise respective groups of optimization routines, where the optimization routines of each respective group of optimization routine of an associated different quantum circuit optimization sequence are applied in a respective defined order of the associated different quantum circuit optimization sequence to an associated respective identical copy of the input raw quantum circuit to produce a respective compiled output quantum circuit, and wherein at least two of the different quantum circuit optimization sequences comprise a same group of optimization routines in different respective defined orders; and
identifies, by the at least one processor, at least one of the different quantum circuit optimization sequences that generates a respective compiled output quantum circuit comprising defined criteria.
2. The system of claim 1 , wherein the at least one of the computer executable components further:
selects, by the at least one processor, the different quantum circuit optimization sequences based on one or more properties of one or more quantum devices that have an ability to execute at least one of the input raw quantum circuit or the respective compiled output quantum circuit comprising the defined criteria.
3. The system of claim 2 , wherein the selecting employs a machine learning model.
4. The system of claim 1 , wherein the defined criteria are selected from a group consisting of a defined quantum circuit based metric and a defined pulse based metric.
5. The system of claim 1 , wherein the at least one of the computer executable components further:
stores, by the at least one processor, in a knowledge base, multiple output quantum circuits generated from execution of the different quantum circuit optimization sequences on the respective identical copies of the input raw quantum circuit, and wherein the multiple output quantum circuits respectively comprise different defined criteria.
6. A computer-implemented method, comprising:
executing in parallel, by at least one processor of a system, different compilers respectively using different quantum circuit optimization sequences on respective identical copies of an input raw quantum circuit to produce respective compiled output quantum circuits for execution on at least one quantum computer, wherein the different quantum circuit optimization sequences comprise respective groups of optimization routines, where the optimization routines of each respective group of optimization routine of an associated different quantum circuit optimization sequence are applied in a respective defined order of the associated different quantum circuit optimization sequence to an associated respective identical copy of the input raw quantum circuit to produce a respective compiled output quantum circuit, and wherein at least two of the different quantum circuit optimization sequences comprise a same group of optimization routines in different respective defined orders; and
identifying, by the at least one processor, at least one of the different quantum circuit optimization sequences that generates a respective compiled output quantum circuit comprising defined criteria.
7. The computer-implemented method of claim 6 , further comprising:
selecting, by the at least one processor, the different quantum circuit optimization sequences based on one or more properties of one or more quantum devices that have an ability to execute at least one of the input raw quantum circuit or the respective compiled output quantum circuit comprising the defined criteria.
8. The computer-implemented method of claim 7 , wherein the selecting employs a machine learning model.
9. The computer-implemented method of claim 6 , wherein the defined criteria are selected from a group consisting of a defined quantum circuit based metric and a defined pulse based metric.
10. The computer-implemented method of claim 6 , further comprising:
storing, by the at least one processor, in a knowledge base, multiple output quantum circuits generated from execution of the different quantum circuit optimization sequences on the respective identical copies of the input raw quantum circuit, wherein the multiple output quantum circuits respectively comprise different defined criteria.
11. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by at least one processor to cause the at least one processor to:
execute in parallel, by the at least one processor, different compilers respectively using different quantum circuit optimization sequences on respective identical copies of an input raw quantum circuit to produce respective compiled output quantum circuits for execution on at least one quantum computer, wherein the different quantum circuit optimization sequences comprise respective groups of optimization routines, where the optimization routines of each respective group of optimization routine of an associated different quantum circuit optimization sequence are applied in a respective defined order of the associated different quantum circuit optimization sequence to an associated respective identical copy of the input raw quantum circuit to produce a respective compiled output quantum circuit, and wherein at least two of the different quantum circuit optimization sequences comprise a same group of optimization routines in different respective defined orders; and
identify, by the at least one processor, at least one of the different quantum circuit optimization sequences that generates a respective compiled output quantum circuit comprising defined criteria.
12. The computer program product of claim 11 , wherein the program instructions are further executable by the at least one processor to cause the at least one processor to:
select, by the at least one processor, the different quantum circuit optimization sequences based on one or more properties of one or more quantum devices that have an ability to execute at least one of the input raw quantum circuit or the respective compiled output quantum circuit comprising the defined criteria.
13. The computer program product of claim 12 , wherein the selecting employs a machine learning model.
14. The computer program product of claim 11 , wherein the defined criteria are selected from a group consisting of a defined quantum circuit based metric and a defined pulse based metric.
15. The computer program product of claim 11 , wherein the program instructions are further executable by the at least one processor to cause the at least one processor to:
store, by the at least one processor, in a knowledge base, multiple output quantum circuits generated from execution of the different quantum circuit optimization sequences on the respective identical copies of the input raw quantum circuit, wherein the multiple output quantum circuits respectively comprise different defined criteria.
16. A system, comprising:
a memory that stores computer executable components; and
at least one processor that executes at least one of the computer executable components that:
generates, by the at least one processor, a knowledge base comprising multiple groups of output quantum circuits, wherein each group is associated with a different input raw quantum circuit, and wherein the generating for each group, comprises:
executing in parallel, by the at least one processor, different compilers respectively using different quantum circuit optimization sequences on respective identical copies of an input raw quantum circuit of the different input raw quantum circuits to produce respective compiled output quantum circuits for execution on at least one quantum device, wherein the different quantum circuit optimization sequences comprise respective groups of optimization routines, where the optimization routines of each respective group of optimization routine of an associated different quantum circuit optimization sequence are applied in a respective defined order of the associated different quantum circuit optimization sequence to an associated respective identical copy of the input raw quantum circuit to produce a respective compiled output quantum circuit, and wherein at least two of the different quantum circuit optimization sequences comprise a same group of optimization routines in different respective defined orders; and
generating, by the at least one processor, metrics associated with the execution of the different compilers respectively using the different quantum circuit optimization sequences on the input raw quantum circuit;
trains, by the at least one processor, using the knowledge base, a machine learning model to generate recommendations of quantum circuit optimization sequences to employ for compiling input quantum circuits into compiled output quantum circuits for execution on quantum devices; and
receives, by the at least one processor, an input quantum circuit; and
generates, by the at least one processor, using the machine learning model, a recommendation specifying a quantum circuit optimization sequence to employ for compiling the input quantum circuit into a compiled output quantum circuit for execution on a quantum device based on defined criteria associated with the metrics.
17. The system of claim 16 , wherein the at least one of the computer executable components further:
selects, by the at least one processor, the different quantum circuit optimization sequences based on one or more properties of one or more quantum devices that have an ability to execute at least one of the input raw quantum circuit or the respective compiled output quantum circuit comprising the defined criteria.
18. The system of claim 16 , wherein the metrics are selected from a group consisting of at least one defined quantum circuit based metric and at least one defined pulse based metric.
19. The system of claim 16 , wherein the output quantum circuits in a group respectively comprise different defined criteria.
20. The system of claim 16 , wherein the generating the recommendation comprises ranking the different quantum circuit optimization sequences based on defined entity criteria.
21. A computer-implemented method, comprising:
generating, by at least one processor of a system, a knowledge base comprising multiple groups of output quantum circuits, wherein each group is associated with a different input raw quantum circuit, and wherein the generating for each group, comprises:
executing in parallel, by the at least one processor, different compilers respectively using different quantum circuit optimization sequences on respective identical copies of an input raw quantum circuit of the different input raw quantum circuits to produce respective compiled output quantum circuits for execution on at least one quantum device, wherein the different quantum circuit optimization sequences comprise respective groups of optimization routines, where the optimization routines of each respective group of optimization routine of an associated different quantum circuit optimization sequence are applied in a respective defined order of the associated different quantum circuit optimization sequence to an associated respective identical copy of the input raw quantum circuit to produce a respective compiled output quantum circuit, and wherein at least two of the different quantum circuit optimization sequences comprise a same group of optimization routines in different respective defined orderers; and
generating, by the at least one processor, metrics associated with the execution of the different compilers respectively using the different quantum circuit optimization sequences on the input raw quantum circuit;
training, by the at least one processor, using the knowledge base, a machine learning model to generate recommendations of quantum circuit optimization sequences to employ for compiling input quantum circuits into compiled output quantum circuits for execution on quantum devices; and
receiving, by the at least one processor, an input quantum circuit; and
generating, by the at least one processor, using the machine learning model, a recommendation specifying a quantum circuit optimization sequence to employ for compiling the input quantum circuit into a compiled output quantum circuit for execution on a quantum device based on defined criteria associated with the metrics.
22. The computer-implemented method of claim 21 , further comprising:
selecting, by the at least one processor, the different quantum circuit optimization sequences based on one or more properties of one or more quantum devices that have an ability to execute at least one of the input raw quantum circuit or the respective compiled output quantum circuit comprising the defined criteria.
23. The computer-implemented method of claim 21 , wherein the defined criteria are selected from a group consisting of at least one defined quantum circuit based metric and at least one defined pulse based metric.
24. The computer-implemented method of claim 21 , wherein the output quantum circuits respectively comprise different defined criteria.
25. The computer-implemented method of claim 21
wherein the generating the recommendation comprises ranking the different quantum circuit optimization sequences based on defined entity criteria.