Photonic quantum computing provides a promising route toward quantum computation by naturally supporting the measurement-based quantum computation (MBQC) model. In MBQC, programs are executed through measurements on a pre-generated graph state, whose construction largely depends on probabilistic fusion operations. However, fusion operations in PQC are vulnerable to two major error sources: fusion failure and fusion erasure. As a result, MBQC compilation must account for both error mechanisms to generate reliable and efficient photonic executions. Prior state-of-the-art MBQC compilation, represented by OneAdapt, is designed for all-photonic architectures and mainly focuses on handling fusion failures. Nevertheless, it does not explicitly model fusion erasures induced by photon loss, which can be substantially more damaging than fusion failures. To mitigate fusion erasure errors, we introduce a new MBQC compilation scheme built upon the spin qubit quantum memory. We propose tree-encoded fusion, an encoding strategy that suppresses erasure errors during graph-state generation. We further incorporate this scheme into a compiler framework with algorithms that reduce the execution overhead of quantum programs. We evaluate the proposed framework using a realistic PQC simulator on six representative quantum algorithm benchmarks across multiple program scales. The results show that tree-encoded fusion achieves better robustness than alternative fusion-encoding strategies, and that our compiler provides exponential improvement over OneAdapt. In addition, we validate the feasibility of our approach through a proof-of-concept demonstration on real PQC hardware.

翻译：光子量子计算通过天然支持基于测量的量子计算（MBQC）模型，为实现量子计算提供了一条有前景的路径。在MBQC中，计算程序通过对预生成的图态进行测量来执行，而图态的构建在很大程度上依赖于概率性融合操作。然而，光子量子计算中的融合操作容易受到两种主要误差源的影响：融合失败和融合擦除。因此，MBQC编译必须同时考虑这两种误差机制，以生成可靠且高效的光子执行方案。此前最先进的MBQC编译方案（以OneAdapt为代表）专为全光子架构设计，主要侧重于处理融合失败。然而，该方案并未明确建模由光子损失引起的融合擦除，而这种误差可能比融合失败更具破坏性。为了抑制融合擦除误差，我们引入了一种基于自旋量子比特量子存储器的新型MBQC编译方案。我们提出了树编码融合（tree-encoded fusion），这是一种在图态生成过程中抑制擦除误差的编码策略。进一步地，我们将该方案集成到一个编译器框架中，并采用相应算法来降低量子程序的执行开销。我们使用一个真实的光子量子计算模拟器，在六个代表性量子算法基准测试（涵盖多个程序规模）上对提出的框架进行了评估。结果表明，树编码融合相比其他替代融合编码策略具有更好的鲁棒性，并且我们的编译器相比OneAdapt实现了指数级性能提升。此外，我们通过在真实光子量子计算硬件上进行概念验证演示，验证了该方法的可行性。