Summary With the growing size of seismic data, high performance computing is critical in seismic processing tasks. In this paper, we leverage quantum computing principles to break beyond classical computing limitations and develop the industry first quantum-based seismic attribute computation algorithm. The experimental results from our algorithm are comparable in quality to classical Sobel-filter based similarity (or coherence) attribute. The algorithm was run on a quantum simulator, an exponential speed up is expected on a real quantum hardware by virtue of the superposition characteristics of quantum bits (qubits). This approach has the potential to change how we do seismic attribute computation in the future, reducing cost and time significantly.
Summary With the growing size of seismic data, high performance computing is critical in seismic processing tasks. In this paper, we leverage quantum computing principles to break beyond classical computing limitations and develop the industry first quantum-based seismic attribute computation algorithm. The experimental results from our algorithm are comparable in quality to classical Sobel-filter based similarity (or coherence) attribute. The algorithm was run on a quantum simulator, an exponential speed up is expected on a real quantum hardware by virtue of the superposition characteristics of quantum bits (qubits). This approach has the potential to change how we do seismic attribute computation in the future, reducing cost and time significantly.
Due to the increasing volume and complexity of seismic datasets, seismic processing is confronted with increasing computational demands, which necessitate the implementation of innovative solutions such as quantum computing. This study applies the Quantum Fourier Transform (QFT), implemented via the Qiskit framework, to two geophysical applications: computing instantaneous seismic attributes and performing Multi-Dimensional Convolution (MDC). We present a workflow for preparing seismic data for QFT processing, demonstrating results with accuracy comparable to conventional Fourier Transform (FT)-based methods. Although executed on a quantum simulator, the approach is compatible with quantum hardware, promising significant performance gains through qubit superposition. This work offers a new framework for integrating quantum algorithms into geophysical processing workflows, highlighting their potential to improve efficiency and scalability as quantum technology advances.
Abstract Mathematical and computational challenges involved in seismic data processing presents an opportunity for early adoption of quantum computation methods for end-to-end seismic data processing. Existing methods of seismic data processing involve processes with exponential complexities that result in approximations as well as conversion of some of the continuous phenomena into a stochastic one. In the classical computation methods, the mentioned approximations and assumptions enable us to obtain acceptable results in commercially viable time. This paper proposes alternatives of the classical computations that exist in the quantum computation ecosystem along with the computational advantages it holds. The paper also presents potential contributions of the petroleum industry towards sustaining the quantum computation technologies. Fundamentally seismic data processing involves solutions for systems of linear equations and its derivatives. Quantum computation ecosystem holds efficient solutions for systems of linear equations. In the frequency domain, Finite-Difference modelling reduces seismic-wave equations to systems of linear equations. In the classical computational setup the seismic acquisition involves treatment of the recorded waves as rays and has limited summation provision for recreating the natural reflection or refraction phenomena that is continuous instead of being a stochastic process. The algorithms in the quantum ecosystem allow us to consider summation of signals from all possible paths between the source and the receiver, by amplitude-probability. In addition to the systems of linear equations and their solution with corresponding methods in the quantum ecosystem the fourier transformation and partial differential equations enable us to decompose the waves and apply the physics equation to obtain the desired objective. Quantum-algorithms facilitate exponential speed-up in seismic data processing. The PDE-constrained optimization inverts subsurface P-wave velocity. While going through the seismic data processing steps it is found that the fourier transformation algorithms are derived as a decomposition of the diagonal matrix. The key difference between the fast fourier transform and the quantum fourier transform is that the quantum fourier transformation is used as the building block of several quantum algorithms. Seismic inversion involves laws of physics and calculation that are guided by the ordinary differential equations. In the quantum computation ecosystem these algorithms for linear ordinary differential equations for linear partial differential equations have the complexity of (1/e), where ‘e’ is the tolerance. The insights brought by successful implementation of end-to-end seismic data processing with algorithms in the quantum computation domain enables us to drill most optimally located wells and hence facilitate cost saving. Even with a reduction of 10% in the total number of wells that we drill, we can possibly fund development of one quantum computer hence ensuring economic sustainability of the technology. The novelty of the presented paper lies in the comparative analysis of the classical methods with its counterparts in the quantum ecosystem. It explains the technological and economical aspects of the technology such that extensive knowledge of quantum technology is not compulsory for grasping its contents.
Abstract ABSTRACT Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within quantum computing, is particularly effective for optimization tasks, as it leverages quantum tunneling to escape local minima and efficiently explore complex energy landscapes. In geosciences, many problems are framed as high-dimensional optimization problems, including seismic inversion, which aims to estimate subsurface impedances from seismic data for accurate geological interpretation and resource exploration. A novel application of quantum computing for seismic inversion is presented, marking the first instance of inverting seismic data to estimate both P-wave and S-wave impedances using a quantum annealer. Building upon the prior work, which demonstrated the estimation of acoustic impedances from post-stack data using a two-step framework, we propose an enhanced workflow capable of inverting both post-stack and pre-stack seismic data in a single step. This advancement significantly reduces the number of qubits per model parameter (from 20 to 5) while improving computational speed (from 20 seconds to 6.3 seconds). The seismic inversion is implemented using the D-Wave Leap hybrid solver, achieving impedance estimation within 4-9 seconds, with the quantum processing unit (QPU) contributing just 0.043-0.085 seconds. Comparative analysis with simulated annealing reveals that quantum annealing produces impedance models closely matching true values in a single epoch, whereas simulated annealing requires 10 epochs for improved accuracy. These findings underscore the transformative potential of quantum computing for real-time, high-precision seismic inversion, marking a crucial step toward fully quantum-driven geophysical solutions.
Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine learning, have been developed to address some of these limitations. Motivated by the potential of quantum computing, we report on our attempt to map one such classical physics-informed algorithm to a quantum framework. The primary goal is to investigate the technical challenges of this mapping, given that quantum algorithms rely on computing principles fundamentally different from those in classical computing. Quantum computers operate using qubits, which exploit superposition and entanglement, offering the potential to solve classically intractable problems. While current quantum hardware is limited, hybrid quantum-classical algorithms-particularly in quantum machine learning (QML)-demonstrate potential for near-term applications and can be readily simulated. We apply QML to subsurface imaging through the development of a hybrid quantum physics-informed neural network (HQ-PINN) for post-stack and pre-stack seismic inversion. The HQ-PINN architecture adopts an encoder-decoder structure: a hybrid quantum neural network encoder estimates P- and S-impedances from seismic data, while the decoder reconstructs seismic responses using geophysical relationships. Training is guided by minimizing the misfit between the input and reconstructed seismic traces. We systematically assess the impact of quantum layer design, differentiation strategies, and simulator backends on inversion performance. We demonstrate the efficacy of our approach through the inversion of both synthetic and the Sleipner field datasets. The HQ-PINN framework consistently yields accurate results, showcasing quantum computing's promise for geosciences and paving the way for future quantum-enhanced geophysical workflows.
Quantum computers utilize the principles of quantum mechanics to process information and have efficient computational costs compared with central-processing-unit-based computers for specific algorithms.The most general-purpose quantum computer, the quantum gate computer, uses quantum gates corresponding to gates made of transistors as computational tools.Quantum annealers, however, utilize quantum tunneling, based on the theory that particles can pass through barriers higher than the particle energy owing to the particle waveform, and it is specialized in performing mathematical optimizations.This study investigated the theoretical possibility of applying seismic exploration data processing techniques and subsurface structure imaging algorithms using quantum computers.However, this study also reveals the limitations of these methods.The results suggest that quantum computers have the potential to perform numerical modeling, seismic data preprocessing, and data enhancement in a time-efficient manner.
This paper proposes a novel hardware acceleration architecture for computation leveraging the principles of quantum state superposition. Traditional computing paradigms rely heavily on sequential processing, often struggling with the computational demands of large-scale parallel problems. We introduce a quantum bit array-based system designed to exploit superposition, enabling the simultaneous execution of multiple computational tasks. The core concept involves constructing an array of interconnected quantum bits (qubits) where each qubit can exist in a superposition of states. This allows for parallel processing of data, significantly enhancing computational speed and efficiency. We detail the theoretical framework, outlining the key mathematical relationships and operational principles. The architecture's potential for accelerating specific algorithms and the associated performance gains are discussed. This research aims to provide a foundational approach for developing future quantum computing hardware, particularly focusing on harnessing the power of quantum superposition.