量子过程层析是量子信息科学研究的基础之一，但其所需的实验资源会随着量子比特的增加而指数增长。考虑到过程矩阵的稀疏性，近年来一种压缩感知量子过程层析方法可以大大降低量子过程层析的成本和后处理时间。但量子通道研究需要同时层析多种量子门，并且每个量子通道在层析过程中都会存在野点数据。该文提出一种分布式压缩感知量子过程层析方法，通过组合稀疏学习的模式能同时进行多量子通道层析，并有效地剔除野点数据。仿真结果表明：相对于单通道的压缩传感量子过程层析，该方法重构的量子过程矩阵保真度高且对野点数据有较强的鲁棒性，改善了层析性能。
Quantum process tomography (QPT) is one of the foundations of quantum information science research, but the required experimental resources during QPT grow exponentially with the number of qubits. Recently, a compressed sensing QPT (CSQPT) was proposed that significantly reduces the required resources and the post-processing time based on the sparseness of the process matrix. However, the quantum channel analysis needs to simultaneously identify a variety of quantum gates and there are always outliers during the QPT process. This paper describes a distributed compressed sensing quantum process tomography (DCSQPT) method to identify the multi quantum channel tomography while effectively attenuating outliers through collaborative sparse learning. Simulations show that this method is robust to outlier data and accurately reconstructs the process matrix compared to the compressed sensing QPT method while significantly improving the quantum process tomography identification speed.
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