Quantum circuit valuation
Systems and techniques that facilitate quantum circuit valuation are provided. In various embodiments, a system can comprise an input component that can access a first quantum circuit. In various embodiments, the system can further comprise a valuation component that can appraise the first quantum circuit based on one or more factors (e.g., frequency factor, complexity factor, resource factor, similarity factor), thereby yielding a value score that characterizes the first quantum circuit. In various instances, the system can further comprise an execution component that can recommend deployment of the first quantum circuit based on determining that the value score exceeds a threshold.
1. A system, comprising:
a memory that stores computer-executable components; and
a processor that executes at least one of computer-executable components that:
trains a neural network to determine value scores for quantum circuits based on factors with respect to other quantum circuits having a defined level of similarity to the quantum circuits; and
determines, using the neural network, a value score for a first quantum circuit in a defined computing context based on the factors with respect to second quantum circuits having the defined level of similarity to the first quantum circuit, wherein the value score indicates a level of value of the first quantum circuit when executed in the defined computing context relative to the second quantum circuits that have a known effectiveness when executed in the defined computing context, wherein the first quantum circuit is different from the second quantum circuits, and wherein the factors comprise a frequency factor based on respective frequencies of execution of the second quantum circuits.
2. The system of claim 1 , wherein the factors further comprise a complexity factor indicative of an amount of quantum circuit complexity of the first quantum circuit.
3. The system of claim 1 , wherein the determining the value score is based on weighted combination of the factors.
4. The system of claim 1 , wherein the factors further comprise a resource factor indicative of an amount of at least one of quantum circuit pre-processing or quantum circuit post-processing needed for execution of the first quantum circuit on a defined quantum hardware configuration.
5. The system of claim 1 , wherein the factors further comprise a similarity factor indicative of an amount of similarity between the first quantum circuit and the second quantum circuits.
6. The system of claim 1 , wherein the at least one of the computer-executable components further:
applies a genetic algorithm to the first quantum circuit based on determining that the value score exceeds a threshold, thereby yielding a third quantum circuit having a second value score that exceeds the value score of the first quantum circuit.
7. The system of claim 1 , wherein the defined level of similarity is based on at least one of gate count, circuit depth, circuit width, resultant quantum state, computation time, number of executions, or technical domain.
8. A computer-implemented method, comprising:
training, by a device operatively coupled to a processor, a neural network to determine value scores for quantum circuits based on factors with respect to other quantum circuits having a defined level of similarity to the quantum circuits; and
determining, by the device, using the neural network, a value score for a first quantum circuit in a defined computing context based on the factors with respect to second quantum circuits having the defined level of similarity to the first quantum circuit, wherein the value score indicates a level of value of the first quantum circuit when executed in the defined computing context relative to the second quantum circuits that have a known effectiveness when executed in the defined computing context, wherein the first quantum circuit is different from the second quantum circuits, and wherein the factors comprise a frequency factor based on respective frequencies of execution of the second quantum circuits.
9. The computer-implemented method of claim 8 , wherein the factors comprise a complexity factor indicative of an amount of quantum circuit complexity of the first quantum circuit.
10. The computer-implemented method of claim 8 , wherein the determining the value score is based on weighted combination of the factors.
11. The computer-implemented method of claim 8 , wherein the factors further comprise a resource factor indicative of an amount of at least one of quantum circuit pre-processing or quantum circuit post-processing needed for execution of the first quantum circuit on a defined quantum hardware configuration.
12. The computer-implemented method of claim 8 , wherein the factors further comprise a similarity factor indicative of an amount of similarity between the first quantum circuit and the second quantum circuits.
13. The computer-implemented method of claim 8 , further comprising:
in response to determining that the value score exceeds a threshold, generating, by the device, using an evolutionary algorithm on the first quantum circuit, a third quantum circuit having a second value score that exceeds the value score of the first quantum circuit.
14. The computer-implemented method of claim 8 , wherein the defined level of similarity is based on at least one of gate count, circuit depth, circuit width, resultant quantum state, computation time, number of executions, or technical domain.
15. A computer program product for facilitating quantum circuit valuation, the computer program product comprising a computer-readable memory having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
train, by the processor, a neural network to determine value scores for quantum circuits based on factors with respect to other quantum circuits having a defined level of similarity to the quantum circuits; and
determining, by the processor, using the neural network, a value score for a first quantum circuit in a defined computing context based on the factors with respect to second quantum circuits having the defined level of similarity to the first quantum circuit, wherein the value score indicates a level of value of the first quantum circuit when executed in the defined computing context relative to the second quantum circuits that have a known effectiveness when executed in the defined computing context, wherein the first quantum circuit is different from the second quantum circuits, and wherein the factors comprise a freqency factor based on respective frequencies of execution of the second quantum circuits.
16. The computer program product of claim 15 , wherein the factors comprise a complexity factor indicative of an amount of quantum circuit complexity of the first quantum circuit.
17. The computer program product of claim 15 , wherein the determining the value score is based on weighted combination of the factors.
18. The computer program product of claim 15 , wherein the factors further comprise a resource factor indicative of an amount of at least one of quantum circuit pre-processing or quantum circuit post-processing needed for execution of the first quantum circuit on a defined quantum hardware configuration.
19. The computer program product of claim 15 , wherein the factors further comprise a similarity factor indicative of an amount of similarity between the first quantum circuit and the second quantum circuits.
20. The computer program product of claim 15 , wherein the program instructions are further executable to cause the processor to:
in response to determining that the value score exceeds a threshold, generating, by the processor, using a machine learning model on the first quantum circuit, a third quantum circuit having a second value score that exceeds the value score of the first quantum circuit.