Quantum RF Perturbation Generator with Game Theory Optimization
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Solution Overview
Problem
Classical computing approaches are limited in processing large amounts of data for automated decision-making in strategic scenarios, requiring more efficient methods to handle complex problems.
Innovation Solution
A quantum computing system that uses a quantum algorithm for game theory analysis, employing a reward matrix and subset summing to make decisions efficiently, utilizing quantum adder and comparator circuits for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing is used to process large amounts of data for automated decision-making, then processing speed and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent uses quantum adder circuits and comparator circuits as intermediary components that facilitate quantum subset summing operations. These intermediary quantum circuits enable the quantum processor to efficiently process reward matrices and perform automated decision-making without requiring direct complex quantum operations for each calculation step.
Solution Approach 2:
The patent segments the automated decision-making process into distinct quantum computational steps: preparing quantum states representing reward matrices, performing quantum subset summing using quantum adder circuits, comparing results using quantum comparator circuits, and extracting decision outcomes. This segmentation allows each component to be optimized independently while working together to solve the overall problem efficiently.
2Measurement precision
If quantum subset summing is used for game theory analysis, then decision-making accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The patent replaces classical mechanical computing operations with quantum mechanical operations. Specifically, it substitutes classical addition and comparison operations with quantum adder circuits and comparator circuits that leverage quantum superposition and entanglement to perform subset summing operations more efficiently, reducing overall computational resource requirements while improving accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of computation by transitioning from classical binary states to quantum states with continuous probability amplitudes. This parameter change allows the system to represent and process game theory reward matrices with higher precision while utilizing quantum parallelism to reduce the computational resources needed for analyzing multiple decision scenarios simultaneously.
Data Source
AI summary
A perturbation radio frequency (RF) signal generator is provided which generates a perturbed RF output signal to cause a signal classification change by an RF signal classifier. The perturbation RF signal generator may 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 signal perturbation models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning signal perturbation model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and generate the perturbed RF output signal based upon the selected deep learning signal perturbation model to cause the signal classification change in the RF signal classifier.