Unlocking new approaches to industrial optimization with quantum computing on C12’s Callisto Emulator

Combinatorial optimization problems (COPs) appear across many industries, from transportation planning to communication networks and defense. These problems require identifying the best choice among an extremely large number of possibilities, which becomes difficult for classical algorithms as data volume grows or conditions evolve rapidly.

At C12, we are developing a new generation of quantum processors built on carbon nanotubes. While this hardware is under active development, our Callisto emulator already allows partners to explore how quantum algorithms could address real operational challenges by modeling the behavior of future spin qubit systems.

Thales, a leader in advanced sensing and radar technologies, brought to the collaboration a concrete problem with strong industrial relevance: the Maximum Weighted Independent Set (MWIS), used in radar tracking to select the most likely target paths within a complex environment. Together, we set out to assess whether quantum approaches could support or improve MWIS workflows and to evaluate the quantum resources required for future deployment.

Understanding the challenge

MWIS is essential for interpreting radar signals, especially in situations with high clutter or many potential tracks. Classical solvers become increasingly strained as the number of signals grows or real-time constraints tighten. This made MWIS a strong candidate for studying quantum optimization techniques on Callisto.

Approach and methodology

Two quantum strategies were examined: QAOA and quantum annealing. For larger problem sizes, QAOA became difficult to scale, so the focus shifted to quantum annealing.

To support this work, Callisto was extended with a time-dependent annealing model, an Ising formulation of MWIS, and noise processes characteristic of spin qubits. Graph decomposition techniques were added to divide large radar instances into smaller subproblems, allowing the emulator to handle systems of several hundred qubits while maintaining physical realism.

What the simulations show

MWIS instances generated from radar tracking frames were evaluated with the annealing engine on Callisto.

Key observations:

• Successful QA execution on simple one-track and two-track MWIS problems, showing consistent behavior aligned with theoretical predictions.
• Observable differences between quantum and classical solver outputs, notably in error profiles and track fluctuations.
• For every-time-frame scenarios, the QA emulator handled increased lambda clutter values, with performance impacts tracked accordingly.
• Estimated quantum annealing time ranged from 50 to 150 µs, with state preparation and readout times around 5–10 ms and 1 µs respectively.

These results show that quantum annealing can be applied to radar-relevant MWIS problems and that Callisto provides a realistic environment for exploring algorithm performance and resource requirements ahead of future hardware.

Moving forward

This collaboration illustrates how quantum computing can contribute to complex optimization tasks and how realistic emulation supports the development of workflows that will transition to scalable quantum processors.

Full report available soon.

‍