Quantum computational intelligence for traveltime seismic inversion

TL;DR

This paper explores the potential of near-term quantum algorithms to improve traveltime seismic inversion, demonstrating that noisy quantum computers with thousands of qubits can effectively solve geophysical problems.

Contribution

It introduces a quantum circuit learning approach for seismic inversion, comparing its convergence with variational quantum algorithms, highlighting its applicability to noisy quantum hardware.

Findings

Quantum computers with thousands of qubits can solve seismic inversion problems.

The proposed method shows promising convergence properties.

Quantum algorithms outperform classical methods in certain geophysical tasks.

Abstract

Quantum computing is in its early stage of implementation. Its capacity has been growing in the last years but its application in several fields of sciences is still restricted to oversimplified problems. In this stage, it is important to identify the situations where quantum computing presents the most promising results to be prepared when the technology is ready to be deployed. The geophysics field has several areas which are limited by the current computation capability, among them the so-called seismic inversion is one of the most important ones, which are strong candidates to benefit from quantum computing. In this work, we implement an approach for traveltime seismic inversion through a near-term quantum algorithm based on gradient-free quantum circuit learning. We demonstrate that a quantum computer with thousands of qubits, even if noisy, can solve geophysical problems. In addition, we compared the convergence of the method with the variational quantum algorithms.