Energy-Scaled Zero-Noise Extrapolation for Gottesman-Kitaev-Preskill Code

• URL: http://arxiv.org/abs/2512.03583v1
• Date: Wed, 03 Dec 2025 09:08:20 GMT
• Title: Energy-Scaled Zero-Noise Extrapolation for Gottesman-Kitaev-Preskill Code
• Authors: Gui-Zhong Luo, Matthew Otten,
• Abstract summary: Energy-Scaled Zero-Noise Extrapolation (ES-ZNE) is a quantum error mitigation protocol that uses the mean photon number of the GKP code as a tunable effective noise parameter.<n>We show that ES-ZNE successfully mitigates finite-energy errors, recovering the ideal expectation values in the shallow-noise regime.<n>These results establish ES-ZNE as a practical, software-based strategy for enhancing the performance of near-term bosonic quantum processors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of Gottesman-Kitaev-Preskill (GKP) codes, an approach to hardware-efficient quantum error correction, is limited by the finite squeezing capabilities of current experimental platforms. To circumvent this hardware demand, we introduce Energy-Scaled Zero-Noise Extrapolation (ES-ZNE), a quantum error mitigation protocol that uses the mean photon number of the GKP code as a tunable effective noise parameter. The protocol measures logical observables at a series of accessible finite energies and extrapolates the results to the ideal, infinite-energy limit using an ansatz based on the code's asymptotic error scaling. Through simulating a GKP qubit under a pure-loss channel, we demonstrate that ES-ZNE successfully mitigates finite-energy errors, recovering the ideal expectation values (within numerical uncertainty) in the shallow-noise regime. Furthermore, by computationally removing artifacts arising from the finite-energy encoding, our method characterizes the intrinsic performance of the ideal GKP code, revealing a sharp error threshold beyond which the code's corrective power diminishes. These results establish ES-ZNE as a practical, software-based strategy for enhancing the performance of near-term bosonic quantum processors, trading sampling overhead for demanding physical resources like high squeezing.

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