RF Signal Classification via Quantum Subset Summing
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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, lacking the efficiency and speed required for complex problems, and there is no quantum equivalent method for subset summing in decision-making.
Innovation Solution
A quantum computing system that employs a game theory reward matrix and quantum subset summing using quantum adder and comparator circuits to make decisions efficiently, converting binary data into qubit format and performing subset summing operations to select the best deep learning model for RF signal classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum subset summing is used for automated decision-making, then processing speed and efficiency are improved, but device complexity increases
Solution Approach 1:
The patent introduces a quantum computing system as an intermediary between the reward matrix input and the decision output. The quantum processor acts as a mediator that transforms classical binary data into quantum states, performs subset summing operations through quantum interference and measurement, and converts results back to classical format. This intermediary quantum system enables exponential speedup in processing strategic scenarios while managing complexity through specialized quantum hardware rather than general-purpose classical systems.
2Measurement precision
If quantum computing is used to process large data sets, then decision-making accuracy is improved, but loss of time for data conversion increases
Solution Approach 1:
The patent implements preliminary action by pre-converting the reward matrix into quantum-friendly formats and preparing quantum states before the actual subset summing operation. The system performs initial data validation, quantum state preparation, and circuit configuration in advance, so that when the quantum processing begins, the data is already optimized for quantum computation. This reduces the perceived conversion time during the critical decision-making phase.
Solution Approach 2:
The patent employs skipping by parallelizing the quantum subset summing operation to evaluate multiple strategic scenarios simultaneously through quantum superposition. Instead of processing scenarios sequentially, the quantum system rushes through the evaluation of numerous possible outcomes in parallel, with the measurement step collapsing the superposition to reveal the optimal decision. This dramatically reduces the effective processing time despite the complexity of data conversion.
Data Source
AI summary
A radio frequency (RF) signal classification device may include an RF receiver configured to receive RF signals, 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 RF signals using the selected deep learning model for RF signal classification.