L3Harris Technologies had two patents in quantum computing during Q1 2024. The patent filed by L3Harris Technologies Inc in Q1 2024 describes a perturbation radio frequency (RF) signal generator that utilizes quantum computing circuitry to select and apply deep learning signal perturbation models in order to cause a signal classification change in an RF signal classifier. This technology aims to enhance signal classification capabilities through the use of quantum computing and deep learning algorithms. 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 grant share with quantum computing as a theme is 50% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Perturbation rf signal generator incorporating quantum computing with game theoretic optimization and related methods (Patent ID: US20240054377A1)

The patent filed by L3Harris Technologies Inc. describes a perturbation radio frequency (RF) signal generator that utilizes a quantum computing circuit and a processor to generate a perturbed RF output signal, causing a signal classification change in an RF signal classifier. The processor generates a game theory reward matrix for various deep learning signal perturbation models, cooperates with the quantum computing circuit to perform quantum subset summing of the matrix, selects a perturbation model based on this analysis, and generates the perturbed RF output signal accordingly. The system aims to enhance signal classification by leveraging quantum computing and deep learning techniques for perturbation signal generation.

The claims associated with the patent detail the specific functionalities and components of the perturbation RF signal generator, including the generation of game theory reward matrices, utilization of different deep learning signal perturbation models with varying weights, and the incorporation of techniques such as Fast Gradient Sign Method (FGSM) and variational autoencoders (VAE). The method outlined in the patent involves generating perturbed signals for different signal classes, constructing a 3D latent space for perturbation models, and utilizing quantum computing for Z-test construction. Overall, the patent focuses on the innovative use of quantum computing and deep learning algorithms to optimize RF signal classification through perturbation signal generation.

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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.