H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
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Abstract (arXiv)
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[partialθ] ∈ 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 15 Jan 2026.
Builds on
5 of 10 references.
References · 5
- 2020Variational Quantum Algorithmscited by 687
- 2018Barren plateaus in quantum neural network training landscapescited by 624
- 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
- 2021Theory of overparametrization in quantum neural networkscited by 100
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Citing papers per year: 2026: 1.
1 paper.