At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyze these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterizing their operation. While classical computational approaches cannot perform like-for-like computations of quantum systems beyond a certain scale, classical high-performance computing (HPC) may nevertheless be useful for precisely these characterization and certification tasks. By developing open-source customized algorithms using HPC, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of 10⁶. This requires finding 10⁸ elements of the matrix corresponding to the positive operator valued measure, the quantum description of the detector, and is achieved in minutes of computation time. Moreover, by exploiting the structure of the problem, we achieve highly efficient parallel scaling, paving the way for quantum objects up to a system size of 10¹² elements to be reconstructed using this method. In general, this shows that a consistent quantum mechanical description of quantum phenomena is applicable at everyday scales. More concretely, this enables the reconstruction of large-scale quantum sources, processes and detectors used in computation and sampling tasks, which may be necessary to prove their nonclassical character or quantum computational advantage.

中文翻译：

---

通过高性能计算实现可扩展的量子探测器层析成像


在大规模上，量子系统在执行某些任务时可能比经典系统更有优势。因此，开发工具以符合量子力学的方式在相关尺度上分析这些系统，对于基准测试性能和表征其操作至关重要。虽然经典计算方法无法对超过一定规模的量子系统执行同类计算，但经典高性能计算 （HPC） 仍然可能对这些表征和认证任务有用。通过使用 HPC 开发开源定制算法，我们在覆盖 10⁶ 希尔伯特空间的超大规模量子光子探测器上执行量子断层扫描。这需要找到矩阵的 10⁸ 个元素，对应于正算子值测度，即探测器的量子描述，并在几分钟的计算时间内完成。此外，通过利用问题的结构，我们实现了高效的并行缩放，为使用这种方法重建系统大小达 10¹² 个元素的量子对象铺平了道路。一般来说，这表明对量子现象的一致量子力学描述适用于日常尺度。更具体地说，这使得能够重建用于计算和采样任务的大规模量子源、过程和探测器，这可能是证明其非经典特性或量子计算优势所必需的。