A four-player potential game for barren-plateau-aware quantum ansatz design
- URL: http://arxiv.org/abs/2604.21955v1
- Date: Thu, 23 Apr 2026 07:58:46 GMT
- Title: A four-player potential game for barren-plateau-aware quantum ansatz design
- Abstract summary: We cast the design of parameterized quantum circuits as a four-player potential game whose state is a circuit directed acyclic graph (DAG)<n>Per-player restricted action sets factorize the move space into append, remove, retype, and rewire operations; a block-coordinate $varepsilon-Nash residual $_textNash$ certifies that no single player can improve unilaterally.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We cast the design of parameterized quantum circuits as a four-player potential game whose state is a circuit directed acyclic graph (DAG) and whose players encode trainability, non-stabilizerness, task performance, and hardware cost. Per-player restricted action sets factorize the move space into append, remove, retype, and rewire operations; a block-coordinate $\varepsilon$-Nash residual $δ_\text{Nash}$ certifies that no single player can improve unilaterally. A single weight sweep on MaxCut $K_4$ traces a Pareto frontier from a Clifford endpoint $(M_2/n,\langle H\rangle)=(0,4.00)$ to a non-Clifford endpoint $(0.48,3.30)$. On three four-qubit hardware topologies (heavy-hex, $2\times 2$ grid, Rydberg all-to-all), Nash search achieves the highest mean potential; on the $2\times 2$ grid Nash reaches the theoretical ceiling $Φ_\text{max}=4.10$ on two of five seeds while the simulated-annealing baseline does so on one; paired Wilcoxon tests over five seeds cannot reject the null on any single topology ($p\ge 0.22$). On LiH/STO-3G, seeding Nash from a 58-gate Givens-doubles ansatz produces a 48-operation, depth-25 circuit retaining $97.7\%$ of the correlation energy while simultaneously reducing gate count, increasing non-stabilizerness, and controlling trainability. The framework is complementary to energy-only searches such as ADAPT-VQE and k-UpCCGSD, which reach chemical accuracy with fewer operations but do not optimize the other three axes.
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