Quantum Applications, Robotics

SandboxAQ tests quantum navigation on Northrop Grumman drone

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September 9, 2026 by Ivy Delaney

Northrop Grumman’s Lumberjack drone completed its first flight test integrating SandboxAQ’s AQNav software, marking the first reported pairing of magnetic and visual navigation systems on an attritable, one-way attack platform. Developed in under fourteen months from its initial flight, the Lumberjack unmanned aircraft system demonstrated a rapid development cycle for expendable unmanned systems designed for contested environments, Northrop Grumman says.

“Today’s platforms need navigation systems they can trust as they operate in increasingly complex and contested environments,” said Max Schuster, program manager, Northrop Grumman. SandboxAQ describes AQNav as hardware-agnostic, enabling swift integration across diverse defense systems.

AQNav Software Integrates Rapidly with Northrop Grumman’s Lumberjack UAS

The recent flight test of Northrop Grumman’s Lumberjack unmanned aircraft system featured a software installation completed in under one hour; engineers integrated SandboxAQ’s AQNav magnetic navigation software into the existing platform. This rapid deployment highlights a key design feature of AQNav, described by Luca Ferrara, General Manager of Navigation at SandboxAQ, as a hardware-agnostic offering that processes sensor data in real-time to determine positioning.

The successful integration demonstrates the potential for swift adaptation of unmanned systems to operate in challenging environments where traditional navigation methods are unavailable, showcasing a streamlined development cycle for attritable unmanned systems. Max Schuster, program manager for the Lumberjack at Northrop Grumman, emphasized the importance of this capability, stating, “Pairing AQNav with Northrop Grumman’s experience in unmanned aircraft and open mission systems will ensure our joint forces have an operationally validated navigation capability in even the most contested domains.”

SandboxAQ has been flight-testing AQNav with military, government, and commercial aerospace partners since 2023, including Airbus and Boeing, and has also collaborated with the United States Air Force for over three years on similar tests aboard C-17 and C-130J aircraft. The company’s work with Northrop Grumman builds upon these integration patterns and aims to deliver scalable, resilient navigation for unmanned operations in GPS-contested environments.

“Leveraging our proven MagNav technologies and drone platform expertise, the flight test with Northrop Grumman further demonstrates the ease by which our AQNav software can be integrated into unmanned systems at the speed and scale required by leading defense organizations,” said Ferrara. AQNav provides continuous positioning without reliance on satellite signals, functioning passively in all weather conditions and across varied terrain.

The software offers two deployment paths: a full-stack solution built natively into platform architecture or a software-only solution for existing systems, expanding accessibility across platforms.

Today’s platforms need navigation systems they can trust as they operate in increasingly complex and contested environments. In collaboration with SandboxAQ, Northrop Grumman is aggressively enhancing our ecosystem of autonomous and unmanned systems with resilient and flight-hardened alternative navigation systems.

Max Schuster, program manager, Lumberjack, Northrop Grumman
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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