Quantum mechanical machine vision system and arithmetic operation method based on quantum dot

Patent metadata

Field	Value
Publication number	US-10489718-B2
Application number	US-201715838597-A
Country	US
Kind code	B2
Filing date	Dec 12, 2017
Priority date	Dec 12, 2016
Publication date	Nov 26, 2019
Grant date	Nov 26, 2019

Abstract

A quantum mechanical arithmetic operation method for machine vision, based on quantum dots is performed by a quantum processing processor. The quantum mechanical arithmetic operation method comprises, obtaining a first labeled graph connecting between feature points of the first image and a second labeled graph connecting feature points of the second image, generating a point-to-point combination by matching the feature points of the first image with the feature points the second image, generating a conflict graph by adding the largest point-to-point combination by comparing the point-to-point combinations with the threshold, generating non-constrained binary optimization equation for finding a maximum independent set of conflict graphs, converting the non-constrained binary optimization equation into Ising model of the quantum system, and calculating the Hamiltonian of Ising model based on the quantum dots to obtain solution of the non-constrained binary optimization equation.

First claim

What is claimed is:

1. A quantum mechanical arithmetic operation method based on quantum dots, the quantum mechanical arithmetic operation method being performed by a quantum processing processor, in a quantum system, the quantum mechanical arithmetic operation method comprising:
obtaining a first labeled graph connecting between feature points of a first image and a second labeled graph connecting feature points of a second image;
generating a point-to-point combination by matching the feature points of the first image with the feature points of the second image;
generating a conflict graph by adding the largest point-to-point combination by comparing the point-to-point combinations with a threshold;
generating a non-constrained binary optimization equation for finding a maximum independent set of conflict graphs;
converting the non-constrained binary optimization equation for finding a maximum independent set of conflict graphs into an Ising model of the quantum system; and
calculating a Hamiltonian of the Ising model based on the quantum dots to obtain a solution of the non-constrained binary optimization equation.

2. The quantum mechanical arithmetic operation method of claim 1 , wherein calculating the Hamiltonian of the Ising model based on the quantum dots to obtain the solution of the non-constrained binary optimization equation is performed by quantum dots arranged in a matrix shape.

3. The quantum mechanical arithmetic operation method of claim 2 , wherein neighboring quantum dots with the shortest distance in a column direction or a row direction are connected to each other through a tunnel junction.

4. The quantum mechanical arithmetic operation method of claim 1 , wherein the Hamiltonian of the Ising model is calculated through adiabatic evolve, in calculating the Hamiltonian of the Ising model based on the quantum dots to obtain the solution of the non-constrained binary optimization equation.

5. The quantum mechanical arithmetic operation method of claim 1 , further comprising:
repeatedly learning the non-constrained binary optimization equation through machine learning.

6. A quantum mechanical machine vision system comprising:
an image acquisition module to acquire an image;
a quantum processing processor to process the image obtained from the image acquisition module; and
a memory unit to store data necessary for computation of the quantum processing processor;
wherein the quantum processing processor,
obtains a first labeled graph connecting between feature points of a first image and a second labeled graph connecting feature points of a second image,
generates a point-to-point combination by matching the feature points of the first image with the feature points the second image,
generates a conflict graph by adding the largest point-to-point combination by comparing the point-to-point combinations with a threshold,
generates a non-constrained binary optimization equation for finding a maximum independent set of conflict graphs,
converts the non-constrained binary optimization equation for finding a maximum independent set of conflict graphs into an Ising model of the quantum mechanical machine vision system, and
calculates a Hamiltonian of the Ising model based on quantum dots to obtain a solution of the non-constrained binary optimization equation.

7. The quantum mechanical machine vision system of claim 6 , wherein the quantum processing processor comprises quantum dots arranged in a matrix shape.

8. The quantum mechanical machine vision system of claim 7 , wherein neighboring quantum dots with the shortest distance in a column direction or a row direction are connected to each other through a tunnel junction.

9. The quantum mechanical machine vision system of claim 7 , wherein the quantum processing processor further comprises a charge detection unit disposed adjacent to the quantum dots.

10. The quantum mechanical machine vision system of claim 6 , wherein the quantum processing processor calculates the Hamiltonian of the Ising model through adiabatic evolve.

Assignees

• Univ Seoul Ind Coop Found

Inventors

• Ahn Do Yeol

Classifications

• G06V30/1916

  Validation; Performance evaluation · CPC title

• G06F18/217

  Validation; Performance evaluation; Active pattern learning techniques · CPC title

• B82Y10/00

  Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic · CPC title

• G06N20/00

  Machine learning · CPC title

• G06V10/36

  Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given point; Non-linear local filtering operations, e.g. median filtering · CPC title