Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices
Quantum Physics
2020-03-13 v3 Data Structures and Algorithms
Machine Learning
Software Engineering
Abstract
Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e., quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q -aware, low-depth quantum circuits that generate Greenberger-Horne-Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.
Cite
@article{arxiv.1801.02363,
title = {Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices},
author = {Davide Ferrari and Michele Amoretti},
journal= {arXiv preprint arXiv:1801.02363},
year = {2020}
}
Comments
14 pages, 13 figures