Efficient and Effective Quantum Compiling for Entanglement-based Machine Learning on IBM Q Devices
This work addresses the need for efficient quantum compiling for entanglement-based machine learning on IBM Q devices, but it is incremental as it focuses on a specific compiler improvement for GHZ states.
The paper tackled the problem of compiling low-depth quantum circuits for generating GHZ entangled states on IBM Q devices, resulting in a compiler that improves upon the QISKit compiler for this specific purpose, as demonstrated by implementing a quantum oracle for learning parity with noise.
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.