Adaptive H-EFT-VA maintains gradient variance Omega(1/poly(N)) during safe Hilbert space expansion, doubling fidelity over static H-EFT-VA on benchmarks up to 14 qubits.
H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
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abstract
Variational Quantum Algorithms (VQAs) are critically threatened by the Barren Plateau (BP) phenomenon. In this work, we introduce the H-EFT Variational Ansatz (H-EFT-VA), an architecture inspired by Effective Field Theory (EFT). By enforcing a hierarchical "UV-cutoff" on initialization, we theoretically restrict the circuit's state exploration, preventing the formation of approximate unitary 2-designs. We provide a rigorous proof that this localization guarantees an inverse-polynomial lower bound on the gradient variance: $Var[\partial\theta] \in \Omega(1/poly(N))$. Crucially, unlike approaches that avoid BPs by limiting entanglement, we demonstrate that H-EFT-VA maintains volume-law entanglement and near-Haar purity, ensuring sufficient expressibility for complex quantum states. Extensive benchmarking across 16 experiments on the Transverse Field Ising Model confirms a 109x improvement in energy convergence and a 10.7x increase in ground-state fidelity over standard Hardware-Efficient Ans\"atze (HEA), with statistical significance of $p < 10^{-88}$. The static framework is most effective for Hamiltonians with moderate reference-state overlap; extension to systems with larger reference-state gaps is addressed through dynamic UV-cutoff relaxation strategies explored in concurrent work.
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quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
Adaptive H-EFT-VA maintains gradient variance Omega(1/poly(N)) during safe Hilbert space expansion, doubling fidelity over static H-EFT-VA on benchmarks up to 14 qubits.