Short depth circuits as quantum classifiers

1. A system, comprising:

quantum hardware;

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

a calibration component that calibrates the quantum hardware to generate a short depth quantum circuit;

a cost function component that determines a cost function for the short depth quantum circuit based on an initial value for a parameter of a machine-learning classifier; and a training component that modifies the initial value for the parameter during training to a second value for the parameter based on the cost function for the short depth quantum circuit.

2. The system of claim 1 , wherein the computer executable components further comprise:

a machine learning component that determines an output of an activation function of the machine-learning classifier based on the second value of the parameter.

3. The system of claim 1 , wherein the training component encodes labeled training information into at least one qubit via a quantum feature map.

4. The system of claim 1 , wherein the short depth quantum circuit utilizes at least one gate that is naturally accessible on the quantum hardware.

5. The system of claim 1 , wherein the training component utilizes a fixed-frequency superconducting qubit architecture to generate an architecture of the machine-learning classifier.

6. The system of claim 1 , wherein the training component implements a measurement scheme for binary label classification of training information.

7. The system of claim 1 , wherein the training component implements a commuting measurement scheme facilitating multi-label classification of training information.

8. A computer-implemented method, comprising:

calibrating, by a computing system, quantum hardware to generate a short depth quantum circuit;

determining, by the system, a first cost function for the short depth quantum circuit based on an initial value for a parameter of a machine-learning classifier; and

modifying, by the system, the initial value for the parameter during training to a second value for the parameter based on the first cost function for the short depth quantum circuit.

9. The computer-implemented method of claim 8 , further comprising:

determining, by the system, a second cost function that indicates a measurement for a quantum binary state discrimination.

10. The computer-implemented method of claim 8 , further comprising:

determining, by the system, a second cost function that identifies a high-probability multi-label classification scheme for k-ary quantum state discrimination.

11. The computer-implemented method of claim 8 , further comprising:

determining, by the system, a second cost function that identifies a Hilbert space and feature space partition for binary data classification.

12. The computer-implemented method of claim 8 , further comprising:

determining, by the system, a second cost function that identifies a Hilbert space and feature space partition for k-ary data classification.

13. The computer-implemented method of claim 8 , further comprising:

determining, by the system, a feature map that prepares a simple input state for the short depth quantum circuit based on training information.

14. The computer-implemented method of claim 8 , further comprising:

selecting, by the system, the short depth quantum circuit from a hardware-efficient circuit family.

15. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing system to cause the computing system to at least:

calibrate quantum hardware to generate a short depth quantum circuit;

determine a cost function for the short depth quantum circuit based on an initial value for a parameter of a machine-learning classifier; and

modify the initial value for the parameter during training to a second value for the parameter based on the cost function for the short depth quantum circuit.

16. The computer program product of claim 15 , wherein the program instructions are further executable by the computing system to cause the computing system to at least:

measure an output statistic of the short depth quantum circuit;

determine a second cost function of a plurality of cost functions based on the output statistic; and

assign the output statistic to the second cost function.

17. The computer program product of claim 16 , wherein the program instructions are further executable by the computing system to cause the computing system to at least:

determine a total cost function based on the second cost function.

18. The computer program product of claim 15 , wherein the program instructions are further executable by the computing system to cause the computing system to at least:

prepare a sample to be classified as an input quantum state with a feature map.

19. The computer program product of claim 18 , wherein the program instructions are further executable by the computing system to cause the computing system to at least:

operate the short depth quantum circuit with the second value of the parameter.

20. The computer program product of claim 19 , wherein the program instructions are further executable by the computing system to cause the computing system to at least:

measure an output from operating the short depth quantum circuit; and

assigning a classification label for the sample to be classified based on the output from operating the short depth quantum circuit.