Integrate hardware architectural characteristics into the F2 model

Ascertain whether the F2 dual-tower offline reinforcement learning model for compiling Trotter-based Hamiltonian simulation circuits can directly incorporate hardware architectural characteristics—such as qubit connectivity and native gate sets—into its learning and decision-making to improve compilation outcomes.

Background

Near-term quantum hardware imposes constraints on connectivity, gate fidelities, and native gate sets, making depth and gate-count minimization critical. F2 currently compiles actions to hardware-compatible circuits via standard decompositions but does not explicitly embed architectural biases into the model’s learning objective or state representation.

The authors explicitly raise whether and how architectural characteristics could be considered by the model, suggesting a potential avenue to align policy learning with hardware-specific constraints and potentially further reduce resource requirements while preserving accuracy.

References

While this progress is promising, multiple research questions are still unanswered. These questions are as follows. Can architectural characteristics be considered by the model?

— F2: Offline Reinforcement Learning for Hamiltonian Simulation via Free-Fermionic Subroutine Compilation  (2512.08023 - Decker et al., 8 Dec 2025) in Section 7 (Conclusion)