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

Abstract

Disclosed are a quantum system-based pattern recognition computation apparatus and method for artificial intelligence or machine learning. The computation apparatus recognizes patterns between input data by using a quantum system. The computation apparatus includes a modeling unit and an interpretation unit. The modeling unit sets up an objective function based on the similarity between a first pattern derived from the relationships between points of interests of a first data and a second pattern derived from the relationships between points of interests of a second data. The interpretation unit finds 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.

What is claimed is : 
     
         1 . 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 data and a second pattern derived from relationships between elements of reference data; 
 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 data matches the reference data 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 pattern recognition computation apparatus of  claim 1 , wherein the program instructions are further configured to:
 vectorize the relationships between the elements of the input data, and generate the first pattern by modeling a set of the vectorized relationships between the elements of input data as the first pattern; and   vectorize the relationships between the elements of the reference data, and generate the second pattern by modeling a set of the vectorized relationships between the elements of the reference data as the second pattern.   
     
     
         3 . The quantum system-based pattern recognition computation apparatus of  claim 1 , wherein the data include text data or voice sound data. 
     
     
         4 . The quantum system-based pattern recognition computation apparatus of  claim 1 , wherein the quantum system comprises an Ising model that depends on dipole interaction of a magnetic body. 
     
     
         5 . The quantum system-based pattern recognition computation apparatus of  claim 4 , wherein the Ising model is a physical model that depends on trapped ion-based spin-phonon coupling. 
     
     
         6 . The quantum system-based pattern recognition computation apparatus of  claim 1 , wherein the quantum system comprises a physical model that has energy corresponding to the objective function. 
     
     
         7 . 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 data and a second pattern derived from relationships between elements of reference data;   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 data matches the reference data 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.   
     
     
         8 . The quantum system-based pattern recognition computation method of  claim 7 , wherein the setting up comprises:
 vectorizing the relationships between the elements of the input data, and modeling a set of the vectorized relationships between the elements of the input data as the first pattern; and   vectorizing the relationships between the elements of the reference data, and modeling a set of the vectorized relationships between the elements of the reference data as the second pattern.   
     
     
         9 . The quantum system-based pattern recognition computation method of  claim 7 , wherein the data include text data or voice sound data. 
     
     
         10 . The quantum system-based pattern recognition computation method of  claim 7 , wherein the quantum system comprises an Ising model that depends on dipole interaction of a magnetic body. 
     
     
         11 . The quantum system-based pattern recognition computation method of  claim 10 , wherein the Ising model is a physical model that depends on trapped ion-based spin-phonon coupling. 
     
     
         12 . The quantum system-based pattern recognition computation method of  claim 7 , wherein the quantum system comprises a physical model that has energy corresponding to the objective function.