Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices

TL;DR

This paper introduces a specialized quantum compiler for IBM Q devices that efficiently generates low-depth GHZ entangled states, enhancing quantum machine learning applications like learning parity with noise.

Contribution

A new device-aware quantum compiler optimized for GHZ state circuits that outperforms the standard QISKit compiler for specific entanglement tasks.

Findings

Improved GHZ circuit implementation on IBM Q devices

Enhanced performance in quantum machine learning tasks

Successful demonstration of learning parity with noise

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.