Insider Brief
- C12 and Thales have developed QuantumTrack, a hybrid quantum-classical approach for real-time multi-target radar tracking that won the 2026 Quantum Effects Award for Quantum Computing Hardware.
- QuantumTrack matched the time-to-solution of the best classical solver in the reported benchmark and was about 100 times faster than competing quantum annealers on C12 ’s Callisto emulator.
- The project has reached TRL 5, with C12 and Thales targeting a TRL 6 demonstration on C12’s physical processor and potential defense-system deployment.
PRESS RELEASE — C12 , the French quantum computing company building carbon nanotube spin-qubit processors, and Thales , the global technology leader in Aerospace, Defence, and Cyber & Digital, today announced QuantumTrack, a hybrid quantum-classical solution for real-time multi-target radar tracking. The project has won the 2026 Quantum Effects Award in the Quantum Computing Hardware category, and the two partners are presenting it at the Quantum Effects trade fair in Stuttgart, Germany, on October 6.
On the benchmark, QuantumTrack matched the time-to-solution of the best classical solver and delivered results around 100 times faster than competing quantum annealers. These results were obtained on Callisto, C12 ‘s quantum emulator, which faithfully reproduces the physical behavior of a processor with up to 20 qubits. QuantumTrack The project reflects a broader, ongoing collaboration between C12 and Thales that spans both science and industry.
“This award is first and foremost a recognition of the work we have been doing with Thales for several years,” said Pierre Desjardins, CEO and co-founder of C12 . “QuantumTrack shows that a quantum processor can take on a real-world operational problem with demanding real-time constraints, and that our carbon nanotube architecture is particularly well suited to this kind of use case. The next step is demonstrating these results on our own chip.”
A Bottleneck for Next-Generation Radar
To track moving objects, a radar must, at each scan, associate thousands of detections with known trajectories while separating real targets from measurement noise and clutter. The reference method, Multiple Hypothesis Tracking (MHT), consists of keeping several candidate scenarios simultaneously before selecting the most likely one. However, the number of possible combinations grows exponentially as the number of tracked objects increases, and the underlying problem is NP-hard. Classical solvers must therefore limit the number of hypotheses to meet real-time constraints, which often means pruning them at each antenna scan, even if some could later become the most valid.
That bottleneck is becoming critical. In civil airspace, the surge in drone traffic is driving demand for new Unmanned Traffic Management (UTM) systems. In defense, evolving
threats, including saturation attacks, are pushing radar and command-and-control (C2) systems to their limits.
“Multi-target tracking lies at the heart of next-generation radar, which must operate in increasingly dense environments, whether managing drone traffic or countering emerging threats,” said Jean-Marc Divanon, Radar CoE Director at Thales . “Together with C12 , we are already exploring how quantum computing can remove this bottleneck, through an approach co-designed all the way from the qubit to the operational system.”
Co-Designed from Qubit to Radar System
QuantumTrack targets the most computationally demanding step of MHT: selecting mutually compatible hypotheses. Rather than running the entire problem on quantum hardware, the teams designed a hybrid workflow for near-term devices. Large problem instances are broken down into subproblems sized for the processor, solved through quantum annealing, then mapped back to the original graph and merged.
The approach leverages a feature specific to C12 ‘s architecture. Its spin qubits, hosted in carbon nanotubes and coupled to a microwave resonator (a spin-cQED architecture), offer high connectivity. They can also be electrically tuned to switch from a memory mode to an operating mode in which the processor acts as a native quantum annealer.
The division of labor follows the same co-design approach: C12 contributes the hardware architecture and scientific expertise, while Thales brings the MHT algorithm, the operational requirements and the path to deployment.
Towards Real-Time Performance
QuantumTrack was benchmarked on two representative radar scenarios featuring noisy measurements and clutter, using Callisto to model the physical behavior of C12 ‘s processor, including errors and decoherence. Using an active qubit reset protocol the teams have already identified, total end-to-end runtime, including problem decomposition, is estimated at around 50 milliseconds, bringing real-time radar tracking on a quantum processor within reach. The processor’s small footprint and CMOS-compatible fabrication also make it a strong candidate for embedded integration in radar systems.
A Clear Roadmap to Deployment
QuantumTrack has been validated at Technology Readiness Level (TRL) 5. The next milestone is a TRL 6 demonstration on C12 ‘s physical processor. The partners are targeting deployment in defense systems, with Thales acting as system integrator and C12 developing a quantum annealing product for large-scale, real-time optimization that can be coupled to radar systems.