Machine learning (ML) classification tasks can be carried out on a quantum computer (QC) using Probabilistic Quantum Memory (PQM) and its extension, Parameteric PQM (P-PQM) by calculating the Hamming distance between an input pattern and a database of $r$ patterns containing $z$ features with $a$ distinct attributes. For accurate computations, the feature must be encoded using one-hot encoding, which is memory-intensive for multi-attribute datasets with $a>2$. We can easily represent multi-attribute data more compactly on a classical computer by replacing one-hot encoding with label encoding. However, replacing these encoding schemes on a QC is not straightforward as PQM and P-PQM operate at the quantum bit level. We present an enhanced P-PQM, called EP-PQM, that allows label encoding of data stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures. We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device. Our complexity analysis shows that the EP-PQM approach requires $O\left(z \log_2(a)\right)$ qubits as opposed to $O(za)$ qubits for P-PQM. EP-PQM also requires fewer gates, reducing gate count from $O\left(rza\right)$ to $O\left(rz\log_2(a)\right)$. For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to 77% fewer qubits than P-PQM for datasets with $a>2$. EP-PQM reduces circuit depth in the range of 60% to 96%, depending on the dataset. The depth decreases further with a decomposed circuit, ranging between 94% and 99%. EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM implementations on NISQ devices. Furthermore, reducing the number of gates speeds up the classification and reduces the noise associated with deep quantum circuits. Thus, EP-PQM brings us closer to scalable ML on a NISQ device.

翻译：机器学习( ML) 的分类任务可以在量子计算机上进行, 使用 Probabtic 量子存储( QC) 和扩展, Parameteric PQM (P- PQM) 计算输入模式和含有美元特性的美元模式数据库之间的 Hamming 距离( $z美元) 。 精确计算时, 特性必须使用一对数编码( 这是用于以 $>2 来存储多分配数据集的记忆密集型PP2( QC) 。 我们很容易在古典计算机上代表多分配数据, 以标签编码取代一对热的编码。 然而, 取代这些在 QC 输入的编码计划并不简单, 因为 PQM 和 P- P- PQQM 运行模式在量位水平上运行。 我们展示了一个强化的 P- QM, 称为 EP- P- PQM, 可以进一步将数据储存到数据存储和检索程序的电路深。 我们展示了一个理想的 QC 和 QO- mQ 数据分析, 需要一个硬的 QQQ 数据。