Rapid Gaussian Boson Sampling Circuit Screening for GKP States Creation via a Two-Stage Machine Learning Surrogate

Quantum Physics [Submitted on 4 Jun 2026] Rapid Gaussian Boson Sampling Circuit Screening for GKP States Creation via a Two-Stage Machine Learning Surrogate Mohammad Amin Khanpour, Hossein Davoodi Yeganeh Gottesman-Kitaev-Preskill (GKP) states are essential non-Gaussian resources for fault-tolerant photonic quantum computing, enabling logical qubit encoding with intrinsic robustness against errors. Several approaches to GKP state preparation have been explored, including measurement-based protocols in circuit QED and trapped-ion systems, cat-state breeding, and photon-subtraction schemes. However, these methods are either restricted to specific platforms or require deep non-Gaussian resource chains with exponentially low success probabilities. Gaussian Boson Sampling (GBS) offers a compelling all-photonic alternative by generating non-Gaussian states through measurement-induced nonlinearity, without the need for matter-based ancilla or active feedforward. Nevertheless, its practical implementation is limited by the exponential computational cost of evaluating matrix hafnians-#P-complete functions that govern photon-number probabilities. To address this challenge, we introduce a two-stage Histogram Gradient Boosting surrogate pipeline that predicts, without any hafnian computation, the optimal heralding pattern, circuit fidelity, and post-selection probability for candidate GBS circuits, while reserving exact quantum simulation exclusively for surrogate-selected candidates. Trained on circuit configurations across 3-5 optical modes, the surrogate achieves 90.0% GKP-detection accuracy on a held-out set, representing a 23.7 percentage-point improvement over the baseline, with a fidelity mean absolute error of 0.032 and a log-scale post-selection probability R^2 = 0.837, reducing the total simulation burden by approximately 90%. Comments: 15 pages, 4 figures, 4 Tables Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2606.05992 [quant-ph]   (or arXiv:2606.05992v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2606.05992 Focus to learn more Submission history From: Hossein Davoodi Yeganeh [view email] [v1] Thu, 4 Jun 2026 10:39:09 UTC (2,972 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-06 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)