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This paper introduces a novel MBQC compilation scheme leveraging spin qubit quantum memory to mitigate fusion erasure errors, a significant challenge in photonic quantum computing. They propose tree-encoded fusion, an encoding strategy designed to suppress erasure errors during graph-state generation. Experimental results on a PQC simulator and real hardware demonstrate that their compiler, incorporating tree-encoded fusion, achieves exponential improvement in robustness compared to the state-of-the-art OneAdapt compiler.
By explicitly addressing often-overlooked fusion erasure errors, this new compilation scheme unlocks exponentially more robust photonic quantum computations.
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.
