Trapped Ion Quantum Machine Vision Pattern Recognition

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Solution Overview

Problem

Current machine vision systems face difficulties in imitating human-level pattern recognition due to the complexity of computational problems, particularly in optimizing the similarity between image patterns, which is classified as an NP-hard problem, limiting their ability to efficiently solve machine vision-related computational challenges.

Innovation Solution

An ion trap-based quantum-mechanical machine vision system and computation method that utilizes an Ising model for quantum computing, specifically employing adiabatic quantum computing to optimize the interaction between relation vectors of principal points of interest, thereby solving the machine vision-related complex computational problem by generating an adiabatic change of the Hamiltonian to find the most optimized pattern recognition.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If quantum computing is used to solve machine vision computational problems, then computation speed is improved, but device complexity increases

Engineering Contradiction:

Improvecomputation speedVSAvoiddevice complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces classical mechanical computing systems with a quantum computing system based on trapped ions. The quantum computer uses quantum mechanical effects (superposition, entanglement, tunneling) to perform computations that would be intractable for classical systems, achieving exponential speedup for certain machine vision problems while accepting the complexity of quantum hardware

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental computational parameters from classical bits to quantum bits (qubits), enabling the system to process machine vision data in a quantum state space. This parameter change allows the system to solve NP-hard problems more efficiently, though it requires sophisticated quantum control mechanisms

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If adiabatic quantum computing is used to optimize pattern recognition, then solution accuracy is improved, but computation time increases

Engineering Contradiction:

Improvepattern recognition accuracyVSAvoidcomputation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by preparing the quantum system in a carefully designed initial state that encodes the machine vision problem structure. The adiabatic evolution then naturally guides the system toward the optimal solution without requiring iterative refinement, achieving high accuracy while minimizing the time spent in suboptimal states

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a universal quantum annealing Hamiltonian that can encode multiple different machine vision problems simultaneously. This universal approach allows the same quantum system to solve various pattern recognition tasks with different accuracy requirements by adjusting problem-specific parameters, balancing computation time and accuracy across different applications

Inventive Principle:

Principle #6Universality (Multi-functionality)

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 the quantum system to efficiently solve machine vision-related complex computational problems by optimizing the similarity between image patterns, providing an exponential speed-up compared to classical methods and improving the accuracy and efficiency of pattern recognition.

Implementation Method 1

employing adiabatic quantum computing to optimize the interaction between relation vectors of principal points of interest

Methodology Applied

Scientific EffectAdiabatic quantum computing:

Implementation Method 2

utilizes an Ising model for quantum computing

Methodology Applied

Scientific EffectQuantum mechanical computation:

Implementation Method 3

trapped ion spin-phonon chains

Methodology Applied

Scientific EffectSpin-phonon coupling:

Data Source

PatentUS10133959B2Machine vision system using quantum mechanical hardware based on trapped ion spin-phonon chains and arithmetic operation method thereof

Publication Date: 2018.11.20 UNIV OF SEOUL IND COOP FOUND

AI summary

Disclosed are a quantum system-based image pattern recognition computation apparatus and method for machine vision and a quantum system-based machine vision apparatus. The computation apparatus recognizes patterns between images in machine vision 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 image and a second pattern derived from the relationships between points of interests of a second image. 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.