Machine vision system using quantum mechanical hardware based on trapped ion spin-phonon chains and arithmetic operation method thereof

1. A quantum system-based image pattern recognition computation apparatus for machine vision, the computation apparatus recognizing patterns between images in machine vision by using a quantum system, the computation apparatus comprising:

a memory configured to store program instructions; and

a processor configured to execute the program instructions, the program instructions, when executed, configured to:

set up an objective function based on similarity between a first pattern derived from relationships between points of interests of an input image and a second pattern derived from relationships between points of interests of a reference image;

find an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized; and

recognize that the input image matches the reference image if the optimized similarity is greater than a predetermined threshold value, wherein

the quantum system comprises a physical model that depends on interaction between dipoles, and

a Hamiltonian is set up as the objective function, wherein the Hamiltonian is adapted to solve the physical model, and the Hamiltonian includes a qubit operator term associating a superposition of qubit states.

2. The quantum system-based image pattern recognition computation apparatus of claim 1 , wherein the program instructions are further configured to:

vectorize the relationships between the points of interests of the input image, and generate the first pattern by modeling a set of the vectorized relationships between the points of interests of the input image as the first pattern; and

vectorize the relationships between the points of interests of the reference image, and generate the second pattern by modeling a set of the vectorized relationships between the points of interests of the the reference image as the second pattern.

3. The quantum system-based image pattern recognition computation apparatus of claim 1 , wherein the quantum system comprises an Ising model that depends on dipole interaction of a magnetic body.

4. The quantum system-based image pattern recognition computation apparatus of claim 3 , wherein the Ising model is a physical model that depends on trapped ion-based spin-phonon coupling.

5. The quantum system-based image pattern recognition computation apparatus of claim 1 , wherein the quantum system comprises a physical model that has energy corresponding to the objective function.

6. A quantum system-based machine vision apparatus, the machine vision apparatus recognizing patterns between images by using a quantum system, the machine vision apparatus comprising:

an optical device configured to receive light radiated from an optical energy source, and acquire an input image by using characteristics of the received light;

a processor configured to:

derive a first pattern from relationships between points of interests of the acquired input image; and

derive a second pattern from relationships between points of interests of a reference image; and

memory configured to store the derived first and second patterns;

wherein the processor is further configured to:

set up an objective function based on similarity between the first pattern and the second pattern stored in the memory;

find an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized; and

recognize that the input image matches the reference image if the optimized similarity is greater than a predetermined threshold value, wherein

the quantum system comprises a physical model that depends on interaction between dipoles, and

a Hamiltonian is set up as the objective function, wherein the Hamiltonian is adapted to solve the physical model, and the Hamiltonian includes a qubit operator term associating a superposition of qubit states.

7. The quantum system-based machine vision apparatus of claim 6 , wherein the processor is further configured to:

vectorize the relationships between the points of interests of the input image, and generate the first pattern by modeling a set of the vectorized relationships between the points of interests of the input image as the first pattern; and

vectorize the relationships between the points of interests of the reference image, and

generate the second pattern by modeling a set of the vectorized relationships between the points of interests of the reference image as the second pattern.

8. A quantum system-based pattern recognition computation apparatus for artificial intelligence or machine learning, the computation apparatus recognizing and optimizing artificial intelligence or machine learning-based patterns, the computation apparatus being connected to a quantum system, the computation apparatus comprising:

a memory configured to store program instructions; and

a processor configured to execute the program instructions, the program instructions, when executed, configured to:

set up an objective function based on similarity between a first pattern derived from relationships between elements of input image and a second pattern derived from relationships between elements of reference image;

find an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized; and

recognize that the input image matches the reference image if the optimized similarity is greater than a predetermined threshold value, wherein

the quantum system comprises a physical model that depends on interaction between dipoles, and

a Hamiltonian is set up as the objective function, wherein the Hamiltonian is adapted to solve the physical model, and the Hamiltonian includes a qubit operator term associating a superposition of qubit states.

9. A quantum system-based image pattern recognition computation method for machine vision, the computation method recognizing patterns between images in machine vision by using a quantum system, the computation method comprising:

setting up an objective function based on similarity between a first pattern derived from relationships between points of interests of an input image and a second pattern derived from relationships between points of interests of a reference image;

finding an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized; and

recognizing that the input image matches the reference image if the optimized similarity is greater than a predetermined threshold value, wherein

the quantum system comprises a physical model that depends on interaction between dipoles, and

a Hamiltonian is set up as the objective function, wherein the Hamiltonian is adapted to solve the physical model, and the Hamiltonian includes a qubit operator term associating a superposition of qubit states.

10. The quantum system-based image pattern recognition computation method of claim 9 , wherein the setting up comprises:

vectorizing the relationships between the points of interests of the input image, and modeling a set of the vectorized relationships between the points of interests of the input image as the first pattern; and

vectorizing the relationships between the points of interests of the reference image, and modeling a set of the vectorized relationships between the points of interests of the reference image as the second pattern.

11. The quantum system-based image pattern recognition computation method of claim 9 , wherein the quantum system comprises an Ising model that depends on dipole interaction of a magnetic body.

12. The quantum system-based image pattern recognition computation method of claim 11 , wherein the Ising model is a physical model that depends on trapped ion-based spin-phonon coupling.

13. The quantum system-based image pattern recognition computation method of claim 9 , wherein the quantum system comprises a physical model that has energy corresponding to the objective function.

14. A quantum system-based pattern recognition computation method for artificial intelligence or machine learning using a computation apparatus recognizing and optimizing artificial intelligence or machine learning-based patterns, the computation method comprising:

setting up, by the computation apparatus, an objective function based on similarity between a first pattern derived from relationships between elements of input image and a second pattern derived from relationships between elements of reference image;

connecting the computation apparatus to a quantum system that is configured to model the first pattern and the second pattern as physical state variables;

finding an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized; and

recognizing that the input image matches the reference image if the optimized similarity is greater than a predetermined threshold value, wherein

the quantum system comprises a physical model that depends on interaction between dipoles, and

a Hamiltonian is set up as the objective function, wherein the Hamiltonian is adapted to solve the physical model, and the Hamiltonian includes a qubit operator term associating a superposition of qubit states.