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Tuesday, Mar 31, 2026
Mugglehead Investment Magazine
Alternative investment news based in Vancouver, B.C.
Quantum sensors guide UK train in GPS-free navigation test
Quantum sensors guide UK train in GPS-free navigation test
Engineers work on the tube in London. Image via Rail UK.

AI and Autonomy

Quantum sensors guide UK train in GPS-free navigation test

The system uses ultra-sensitive quantum sensors to measure motion and rotation

Great Britain has taken a significant step toward modernizing rail navigation after testing a quantum-based positioning system on a live passenger train.

Announced March 24, the country became the first to trial the technology on a mainline route, signaling a potential shift away from traditional satellite navigation.

Engineers deployed the Railway Quantum Inertial Navigation System on a Great Northern service running between London and Welwyn Garden City on March 3. The trial, led by Great British Railways, tested how well the system could track a train’s position without relying on GPS.

The system uses ultra-sensitive quantum sensors to measure motion and rotation. These sensors calculate position continuously, even when satellite signals fail. Consequently, trains could maintain accurate navigation in tunnels or dense urban corridors. Traditional GPS systems depend on external satellite signals, which can weaken or drop entirely. However, quantum inertial systems operate independently, making them more resilient. Additionally, they reduce exposure to signal interference or outages.

Quantum navigation works by tracking a train’s movement using ultra-sensitive sensors instead of satellites. It measures tiny changes in motion and direction to calculate position in real time. According to Network Rail, this allows trains to keep accurate location data even in tunnels or areas where GPS signals fail.

The trial also tested whether the system could replace fixed trackside infrastructure. These installations often require high upfront costs and ongoing maintenance. In addition, they remain vulnerable to weather damage and technical failures. The technology rode onboard a train operated by Govia Thameslink Railway during the test.

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Quantum navigation could lower infrastructure costs

Engineers collected operational data to evaluate performance under real conditions. Meanwhile, researchers analyzed how the system handled speed changes and route variations.

Transport Minister Lord Peter Hendy said the railway has a long history of technological innovation. He suggested this development continues that tradition while pushing toward more advanced systems. He also indicated the technology could improve reliability and reduce disruptions.

Hendy added that quantum navigation could lower long-term infrastructure costs. Additionally, it may prevent failures by reducing reliance on trackside equipment. Consequently, operators could deliver more consistent service across the network. The trial builds on earlier work by the UK’s Ministry of Defence and Transport for London. These earlier tests explored quantum sensing in controlled environments. However, this marks one of the first demonstrations on an active national rail line.

A consortium led by MoniRail is developing the system alongside several research institutions. These include Imperial College London, the University of Sussex and the National Physical Laboratory. In addition, PA Consulting and QinetiQ are contributing technical expertise.

Government agencies are backing the program through Innovate UK and the Department for Science, Innovation and Technology. Meanwhile, Great British Railways’ innovation arm, GBRX, is coordinating efforts to accelerate deployment. GBRX managing director Toufic Machnouk said live network testing plays a critical role in development. He explained that real-world conditions help turn experimental tools into operational systems. Furthermore, he emphasized collaboration between industry, academia and government.

Quantum sensing has become a priority technology for the UK government. Rail networks offer a complex environment to refine and scale these tools. Additionally, lessons learned here could extend into other transport and infrastructure systems.

The test represents an early phase in development, but it demonstrates practical progress.

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