论文标题
近似量子随机访问记忆体系结构
Approximate Quantum Random Access Memory Architectures
论文作者
论文摘要
使用众所周知的量子算法在许多应用中，量子至高无上都取决于量子格式的数据的可用性。量子随机访问存储器（QRAM），相当于经典的随机访问存储器（RAM），可以满足此要求。但是，现有的QRAM提案要么需要QUTRIT技术和/或引起访问挑战。我们提出了一个基于近似参数量子电路（PQC）的QRAM，该QRAM以地址线作为输入，并在这些地址线中以输出为输出中提供相应的数据。我们介绍了提出的基于PQC的QRAM的两个应用程序，即二进制数据的存储和机器学习的存储（ML）数据集用于分类。
Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. Quantum Random Access Memory (QRAM), an equivalent of classical Random Access Memory (RAM), fulfills this requirement. However, the existing QRAM proposals either require qutrit technology and/or incur access challenges. We propose an approximate Parametric Quantum Circuit (PQC) based QRAM which takes address lines as input and gives out the corresponding data in these address lines as the output. We present two applications of the proposed PQC-based QRAM namely, storage of binary data and storage of machine learning (ML) dataset for classification.
