A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State

Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing. There are two schemes for generating graph states - probabilistic scheme and deterministic scheme. While the all-photonic probabilistic scheme has garnered significant attention, the emitter-photonic deterministic scheme has been proved to be more scalable and feasible across several hardware platforms. This paper studies the GraphState-to-Circuit compilation problem in the context of the deterministic scheme. Previous research has primarily focused on optimizing individual circuit parameters, often neglecting the characteristics of quantum hardware, which results in impractical implementations. Additionally, existing algorithms lack scalability for larger graph sizes. To bridge these gaps, we propose a novel compilation framework that partitions the target graph state into subgraphs, compiles them individually, and subsequently combines and schedules the circuits to maximize emitter resource utilization. Furthermore, we incorporate local complementation to transform graph states and minimize entanglement overhead. Evaluation of our framework on various graph types demonstrates significant reductions in CNOT gates and circuit duration, up to 52% and 56%. Moreover, it enhances the suppression of photon loss, achieving improvements of up to $\times 1.9$.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11132949
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State

Antonio Barbalace, Zhiding Liang, Yuexun Huang, Xiangyu Ren
Neural Networks and Reservoir Computing
article

A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State

Antonio Barbalace, Zhiding Liang, Yuexun Huang, Xiangyu Ren
article en

Abstract

Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing. There are two schemes for generating graph states - probabilistic scheme and deterministic scheme. While the all-photonic probabilistic scheme has garnered significant attention, the emitter-photonic deterministic scheme has been proved to be more scalable and feasible across several hardware platforms. This paper studies the GraphState-to-Circuit compilation problem in the context of the deterministic scheme. Previous research has primarily focused on optimizing individual circuit parameters, often neglecting the characteristics of quantum hardware, which results in impractical implementations. Additionally, existing algorithms lack scalability for larger graph sizes. To bridge these gaps, we propose a novel compilation framework that partitions the target graph state into subgraphs, compiles them individually, and subsequently combines and schedules the circuits to maximize emitter resource utilization. Furthermore, we incorporate local complementation to transform graph states and minimize entanglement overhead. Evaluation of our framework on various graph types demonstrates significant reductions in CNOT gates and circuit duration, up to 52% and 56%. Moreover, it enhances the suppression of photon loss, achieving improvements of up to $\times 1.9$.

Rensselaer Polytechnic Institute (US), University of Chicago (US), University of Edinburgh (GB)
Openalex Percentile: Top 87%
Neural Networks and Reservoir Computing
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