Quantum Object Detection Using Game-Theoretic Model Selection
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Solution Overview
Problem
Classical computing approaches are limited by memory, time, and processing constraints in handling large data sets for automated decision-making in strategic scenarios, necessitating a more efficient quantum computing solution.
Innovation Solution
Implementing a quantum computing system that uses a game theory reward matrix and subset summing operations through quantum adder and comparator circuits to make decisions efficiently and quickly, leveraging quantum processors and variational autoencoders for image data processing and deep learning model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing is used for automated decision-making, then processing speed and efficiency are improved, but device complexity increases
Solution Approach 1:
The patent replaces classical mechanical computing systems with quantum computing systems that utilize quantum mechanical phenomena (superposition, entanglement, interference) to perform computations. This substitution enables exponential speedup in processing certain types of problems while managing the inherent complexity through specialized quantum algorithms and hardware design.
2Loss of time
If classical computing is used to handle large data sets, then device complexity is maintained, but processing time and memory constraints worsen
Solution Approach 1:
The patent changes the fundamental parameters of computation by transitioning from classical bits to quantum bits (qubits), enabling the system to process large datasets in parallel through quantum superposition. This parameter change allows exponential scaling of processing capacity while managing memory constraints through quantum algorithms that require fewer resources for certain computational tasks.
3Measurement precision
If quantum subset summing operations are performed on game theory reward matrices, then decision-making accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The patent applies partial quantum operations to game theory reward matrices, performing quantum subset summing only on critical portions of the decision space rather than exhaustive processing. This approach achieves high decision-making accuracy for strategic scenarios while managing computational resource requirements by focusing quantum computational power where it provides the most benefit.
Data Source
AI summary
An object detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The object detection device may also include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the new image data using the selected deep learning model for object detection.