00mjk / quantumqueryoptimization

Accompanying code for the "Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing" paper

Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing

Tobias Fankhauser, Marc E. Solèr, Rudolf M. Füchslin, Kurt Stockinger, Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing, arXiv preprint arXiv: 2107.10508 (2021) https://arxiv.org/abs/2107.10508

Prerequisites

• Python 3.8
• Anaconda
• Jupyter Notebook
• Qiskit

The Qiskit package can be installed with pip install qiskit.

To utilize a IBM Quantum Computer, a IBM Token is required. It can be acquired from IBM Quantum Computing. Then, the token must be saved once to the computer using

from qiskit import IBMQ
IBMQ.save_account('MY_API_TOKEN')

Programs

• QAOA_Qiskit_main.py is the plain-vanilla implementation of QAOA with Qiskit using default optimizers
• QAOA_FOURIER.py uses the improved FOURIER optimizer by Zhou et al. which is considerably better
• QAOA_PL_main.py uses PennyLane , a Quantum Machine Learning framework
• GAS_main.py is an implementation of [https://arxiv.org/abs/1912.04088](Grover Adaptive Search), an algorithm similar to QAOA

Notes

• The ibmq-melbourne quantum computer is retired by 7 July 2021. As there is currently no other system with that many qubits, experiments with ~15 qubits cannot be carried out.