EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates. (arXiv:2201.07265v1 [cs.ET])

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 …

arxiv qubits