Evaluating state-of-the-art cloud quantum computers for quantum neural networks in gravitational waves data analysis

TL;DR

This paper evaluates the feasibility of using cloud-based quantum computers for running quantum neural networks in gravitational wave data analysis, highlighting costs, implementation challenges, and preliminary results.

Contribution

It demonstrates practical attempts to deploy a quantum neural network on cloud quantum computers for gravitational wave detection, addressing cost, accessibility, and technical issues.

Findings

Achieved 99% fidelity on 3-qubit feature map on IBM and IQM quantum computers.

Obtained 20% prediction accuracy on 4-qubit QNN segment.

Identified major challenges including device availability and library compatibility.

Abstract

In this work, we explore the possibility of using quantum computers provided for usage in cloud by big companies (such as IBM, IonQ, IQM Quantum Computers, etc.) to run our quantum neural network (QNN) developed for data analysis in the context of LISA Space Mission, developed with the Qiskit library in Python. Our previous work demonstrated that our QNN learns patterns in gravitational wave (GW) data much faster than a classical neural network, making it suitable for fast GW signal detection in future LISA data streams. Analyzing the fees from hardware providers like IBM Quantum, Amazon Braket and Microsoft Azure, we found that the fees for running the first segment of our QNN sum up to \$2000, \$60000, and \$1000000 respectively. Using free plans, we succeed to run the 3-qubit feature map of the QNN for one random data sample on {\fontfamily{qcr} \selectfont ibm\_kyoto} and {\fontfamily{qcr}\selectfont IQM Quantum Computers\_Garnet} quantum computers, obtaining a fidelity of 99\%; we could also run the first prediction segment of our QNN on {\fontfamily{qcr} \selectfont ibm\_kyoto}, implemented for 4 qubits, and obtained a prediction accuracy of 20\%. We queried providers such as IBM Quantum, Amazon Braket, Pasqal, and Munich Quantum Valley to obtain access to their plans, but, with the exception of Amazon Braket, our applications remain unanswered to this day. Other major setbacks in using the quantum computers we had access to included Qiskit library version issues (as in the cases of IBM Quantum and IQM Quantum Computers) and the frequent unavailability of the devices, as was the case with the Microsoft Azure provider. All the results presented in this paper were accumulated in 2024.