Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication Related

Topics

Quantum Machine Learning

Approximate Quantum Random Access Memory Architectures

arXiv

Authors: Koustubh Phalak, Junde Li, Swaroop Ghosh

Year

2022

Paper ID

58069

Status

Preprint

Abstract Read

~2 min

Abstract Words

Citations

N/A

Abstract

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2022 reference point for readers tracking recent quantum research.
Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

[1] DOI https://doi.org/arXiv:2210.14804 [2] arXiv https://arxiv.org/abs/2210.14804 [3] Publisher https://arxiv.org/abs/2210.14804

Local Citation Graph (Related-Paper Links)

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0

Likes: 0 Dislikes: 0

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.