Insider Brief
- Infleqtion secured a $1 million Phase II contract from the U.S. Navy to advance its QuIRC machine learning platform for RF signal processing.
- The platform uses contextual machine learning to improve analysis of complex RF environments while reducing data storage and transmission needs.
- The project will develop an integrated prototype for testing in operational Navy environments, building on prior feasibility results.
PRESS RELEASE — Infleqtion (NYSE: INFQ), a global leader in quantum computing and quantum sensing powered by neutral-atom technology, announced that the U.S. Navy has awarded the company a $1 million contract to advance its Quantum-Inspired Rapid Context (QuIRC) machine learning software platform for radio frequency (RF) signal processing.
The program will support the development of advanced RF signal processing capabilities that enhance situational awareness in complex RF environments. The Phase II award builds on a successful Phase I feasibility demonstration and will expand the effort to develop an integrated prototype for testing and evaluation in operationally relevant Navy environments.
“Modern RF environments are dense, dynamic, and increasingly difficult to interpret,” said Pranav Gokhale, Chief Technology Officer at Infleqtion. “Our contextual machine learning approach allows systems to understand signals within their operational context, dramatically reducing the data that needs to be stored or transmitted while preserving the information needed for rapid decision-making. This Phase II award allows us to advance QuIRC from feasibility to a deployable capability that can support mission-critical situational awareness.”
QuIRC is a context-aware, self-learning data pre-processing platform designed to enable analysis and decision-making across high-throughput RF data streams. The platform is powered by Infleqtion’s patent-pending GPU-hosted Contextual Machine Learning (CML) technology, which enables machine learning models to capture contextual correlations across large RF datasets while significantly reducing computational and storage requirements.
In previous demonstrations with the Navy, Infleqtion validated significant reductions in RF signal storage requirements while maintaining high accuracy in downstream signal analysis tasks. With Infleqtion’s new contract, the team will expand the platform by introducing self-learning capabilities that dynamically adapt QuIRC based on contextual feedback. Infleqtion was the only company from Phase I selected to advance to Phase II.
To learn more about Infleqtion’s contextual machine learning technology and its applications, visit www.infleqtion.com
