Spectral amplification for ground-state energy estimation of electronic structure in first quantization

• URL: http://arxiv.org/abs/2607.15358v1
• Date: Thu, 16 Jul 2026 18:01:29 GMT
• Title: Spectral amplification for ground-state energy estimation of electronic structure in first quantization
• Abstract summary: We demonstrate a gate complexity improvement in first-quantized ground-state energy estimation of electronic structure Hamiltonians in a plane wave basis.<n>The improvement relies on identifying a sum-of-squares representation of the Hamiltonian which provides a lower bound certificate and low cost block encoding that leads to a provably lower quantum phase estimation gate cost.<n>The reduction in block encoding normalization and similar block encoding costs to prior work is demonstrated to reduce resource estimates for materials and chemical systems by a factor of $2 - 44times$ corresponding to the lowest cost estimates for ab initio materials simulation.
• Score: 0.38107692506263574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We demonstrate an asymptotic gate complexity improvement in first-quantized ground-state energy estimation of electronic structure Hamiltonians in a plane wave basis by employing the sum-of-squares spectral gap amplification protocol. The improvement relies on identifying a sum-of-squares representation of the Hamiltonian which provides a lower bound certificate and low cost block encoding that leads to a provably lower quantum phase estimation gate cost. This is achieved by using a sum-of-squares operator generated by the total charge density operator resulting in a block encoding normalization improvement of $λ= \mathcal{O}\left(ηΔ^{-1.5}+η^{1.5}Δ^{-1} \right)$ compared to prior work $λ= \mathcal{O}(ηΔ^{-2}+η^2Δ^{-1})$ where $η$ is the number of electrons and $Δ$ is the simulation grid spacing. The asymptotic reduction in block encoding normalization and similar block encoding costs to prior work is demonstrated to reduce resource estimates for materials and chemical systems by a factor of $2 - 44\times$ corresponding to the lowest cost estimates for ab initio materials simulation.

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