At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyse these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterising 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 characterisation and certification tasks. By developing open-source customised algorithms using high-performance computing, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of $10^6$. This requires finding $10^8$ elements of the matrix corresponding to the positive operator valued measure (POVM), 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 $10^{12}$ 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.

翻译：在大尺度上，量子系统在执行特定任务时可能展现出优于经典系统的优势。因此，开发与量子力学一致、在相关尺度上分析这些系统的工具，对于基准测试性能及表征其运行至关重要。尽管经典计算方法无法在超过一定尺度后对量子系统进行同类计算，但经典高性能计算（HPC）或许恰恰可用于这些表征与认证任务。通过利用高性能计算开发开源定制算法，我们实现了对覆盖$10^6$维希尔伯特空间的兆尺度量子光子探测器的量子层析成像。这需要找出对应探测器量子描述的正算子值测度（POVM）矩阵的$10^8$个元素，且计算时间仅为数分钟。此外，通过利用问题结构，我们实现了高效的并行扩展，为使用该方法重建系统规模达$10^{12}$个元素的量子对象铺平了道路。总体上，这表明量子现象的量子力学一致描述适用于日常尺度。更具体而言，该方法使得用于计算和采样任务的大规模量子源、量子过程及量子探测器的重建成为可能，而这可能是证明其非经典特性或量子计算优势所必需的。