Orbital Qubit Machine Vision Solving NP-Hard Pattern Recognition

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Solution Overview

Problem

Current machine vision systems face challenges in simulating human visual recognition abilities, particularly in pattern recognition tasks involving numerous feature points, which are classified as NP-hard problems, requiring complex and computationally intensive calculations.

Innovation Solution

A quantum mechanical machine vision system based on orbital qubits is developed, utilizing a quantum processing processor to convert the non-constrained binary optimization equation into an Ising model, calculating the Hamiltonian to solve the NP-hard problem efficiently by leveraging adiabatic quantum computation and machine learning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If quantum error correction operations are implemented in circuit model quantum computers, then computational accuracy is improved, but qubit coherence time requirement increases to 1000 times one gate time

Engineering Contradiction:

Improvecomputational accuracyVSAvoidqubit coherence time

Core Design Contradiction:

ReliabilityVSDuration of action of stationary object

Solution Approach 1:

The patent replaces the circuit model quantum computer architecture with a quantum annealing computer architecture. This substitution eliminates the need for quantum error correction operations and their associated coherence time requirements, directly resolving the contradiction between computational accuracy and qubit coherence time demands.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the operational parameters of the quantum system by transitioning from gate-based quantum computation to quantum annealing. This parameter change fundamentally alters the computational approach, allowing solution of NP-hard problems through energy minimization without requiring long coherence times for error correction.

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If classical computation methods are used for NP-hard problems in machine vision, then system complexity is maintained, but computational time and resources increase exponentially

Engineering Contradiction:

Improvesystem complexityVSAvoidcomputational time

Core Design Contradiction:

Device complexityVSLoss of time

Solution Approach 1:

The patent substitutes classical computational systems with a quantum annealing computer system. This replacement enables exponential speed-up in solving NP-hard problems such as image identification and pattern recognition, dramatically reducing computational time while maintaining manageable system complexity through quantum mechanical principles.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If the number of feature points for image identification increases, then recognition accuracy is improved, but computational complexity increases as an NP problem

Engineering Contradiction:

Improverecognition accuracyVSAvoidcomputational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces classical computational approaches with quantum annealing computation. This substitution enables the system to handle increased numbers of feature points for improved recognition accuracy without experiencing exponential growth in computational complexity, as quantum annealing efficiently solves the underlying optimization problems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables efficient calculation of complex image identification tasks by transforming the NP problem into a solvable Hamiltonian using orbital qubits, providing an exponential speed-up in solving NP-hard problems compared to classical methods.

Implementation Method 1

calculating the Hamiltonian of Ising model based on an orbital qubit to obtain solution of the non-constrained binary optimization equation

Methodology Applied

Scientific EffectAdiabatic quantum computation:

Implementation Method 2

qubits are required to be coherent for a longer period of time than a single-gate time

Methodology Applied

Scientific EffectQuantum coherence:

Implementation Method 3

concurrently tracks a configuration of a superposition state

Methodology Applied

Scientific EffectQuantum superposition:

Data Source

PatentUS10482387B2Quantum mechanical machine vision system and arithmetic operation method based on orbital qubit

Publication Date: 2019.11.19 UNIV OF SEOUL IND COOP FOUND

AI summary

A quantum mechanical arithmetic operation method for machine vision, based on orbital qubit 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 an orbital qubit to obtain solution of the non-constrained binary optimization equation.