Evolutionary Qubit Selection for Local Cost Functions in Variational Quantum Circuits
Variational quantum circuits (VQCs) are flexible models for optimization and learning based on parameterized quantum circuits trained via cost minimization. A key challenge in their optimization is the presence of barren plateaus, where gradients vanish exponentially with the number of qubits, severely limiting trainability. Local cost functions mitigate this issue by restricting evaluation to qubit subsets, improving gradient scaling. In this work, we propose an evolutionary strategy-inspired method to automatically identify informative qubit subsets for constructing local cost functions. Operating at the level of qubit selection rather than parameter optimization, the approach enables adaptive, circuit-aware objective design. Numerical experiments using the PennyLane framework show that the method alleviates barren plateaus and improves optimization performance in the identity-gate learning problem.