L3Harris Technologies had four patents in quantum computing during Q2 2024. L3Harris Technologies Inc filed patents for radio frequency signal classification devices and cognitive radio devices utilizing quantum computing for deep learning model selection. The devices include RF receivers, quantum computing circuits, processors, and game theory reward matrices to process RF signals for classification and selective operation of RF transmitters based on the selected deep learning model. GlobalData’s report on L3Harris Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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L3Harris Technologies had no grants in quantum computing as a theme in Q2 2024.
Recent Patents
Application: Rf signal classification device incorporating quantum computing with game theoretic optimization and related methods (Patent ID: US20240160978A1)
The patent filed by L3Harris Technologies Inc. describes a radio frequency (RF) signal classification device that utilizes quantum computing to enhance deep learning models for RF signal processing. The device includes an RF receiver, a quantum computing circuit for quantum subset summing, and a processor that generates a game theory reward matrix for different deep learning models. The processor cooperates with the quantum computing circuit to select the most suitable deep learning model based on the quantum subset summing results and processes RF signals for classification using the selected model.
The claims associated with the patent detail the specific components and functionalities of the RF signal classification device, such as the inclusion of signal class rows and classification probability columns in the game theory reward matrix, the types of classification probabilities used (such as VAE cluster Z-test scores, ResNet classifications, and probabilities from distilled classification networks), and the incorporation of modulation class probabilities and waveform class probabilities. The method outlined in the patent involves receiving RF signals, generating a game theory reward matrix for deep learning models, performing quantum subset summing, selecting a model based on the results, and processing RF signals for classification. Overall, the patent focuses on leveraging quantum computing to optimize deep learning models for RF signal classification.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
