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

2025-03-20

AI Summary

This work addresses two key bottlenecks in compiling graph states to quantum circuits on deterministic emitter–photonic hardware: (1) neglect of physical constraints leading to impractical implementations, and (2) poor scalability for large-scale graphs. We propose the first scalable, hardware-aware compilation framework tailored to emitter–photonic platforms, integrating subgraph partitioning, distributed compilation, circuit-level timing scheduling, and local complementation–based graph transformations to jointly optimize graph-state compilation and physical resource utilization (emitters and photons). A novel local-complementation–driven strategy minimizes entanglement overhead, augmented by CNOT gate compression and precise timing modeling. Experimental evaluation across diverse graph topologies demonstrates a 52% reduction in CNOT count, a 56% decrease in circuit depth, and up to 1.9× improvement in photon-loss resilience compared to prior approaches.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 x1.9.

Problem

• Scalable compilation for emitter-photonic graph states
• Optimizing quantum hardware resource utilization
• Reducing CNOT gates and circuit duration

Innovation

• Partitions graph states into subgraphs for compilation
• Uses local complementation to minimize entanglement overhead
• Enhances photon loss suppression by up to x1.9