论文标题
参数概率量子存储器
Parametric Probabilistic Quantum Memory
论文作者
论文摘要
概率量子存储器（PQM）是一个数据结构，该数据结构从二进制输入到存储在存储器上的所有二进制模式的距离。这种数据结构允许启发式方法的发展加快人工神经网络体系结构的选择。在这项工作中，我们提出了PQM的改进的参数版本，以执行模式分类，并且还提供了适用于噪音中间尺度量子（NISQ）计算机的PQM量子电路。我们介绍了公共基准数据集中参数PQM网络分类器的经典评估。我们还执行实验，以验证PQM在5 Q量量子计算机上的可行性。
Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer.
