Quantum smoothed particle hydrodynamics algorithm inspired by quantum walks
- URL: http://arxiv.org/abs/2503.05393v2
- Date: Tue, 08 Apr 2025 10:32:12 GMT
- Title: Quantum smoothed particle hydrodynamics algorithm inspired by quantum walks
- Authors: R. Au-Yeung, V. M. Kendon, S. J. Lind,
- Abstract summary: We propose a quantum algorithm for the time-dependent smoothed particle hydrodynamics (SPH) method.<n>Our algorithm uses concepts from discrete-time quantum walks to solve the one-dimensional advection partial differential equation.<n>We construct a quantum circuit to carry out the calculations for a two-particle system over one, two and three timesteps.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent years have seen great progress in quantum computing, providing opportunities to overcome computational bottlenecks in many scientific applications. In particular, the intersection of computational fluid dynamics (CFD) and quantum computing has become an active area of research with exponential computational speedup as an ultimate goal. In this work, we propose a quantum algorithm for the time-dependent smoothed particle hydrodynamics (SPH) method. Our algorithm uses concepts from discrete-time quantum walks to solve the one-dimensional advection partial differential equation via an SPH formalism. Hence, we construct a quantum circuit to carry out the calculations for a two-particle system over one, two and three timesteps. We compare its outputs with results from the classical SPH algorithm and show there is excellent agreement. The methodology and findings here are a key step towards developing a more general quantum SPH algorithm for solving practical engineering problems on gate-based quantum computers.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
This thesis develops two main techniques to reduce the quantum computational resource requirements.
The aim is to scale up application sizes on current quantum processors.
While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
arXiv Detail & Related papers (2024-03-01T19:36:35Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Quantum algorithms for grid-based variational time evolution [36.136619420474766]
We propose a variational quantum algorithm for performing quantum dynamics in first quantization.
Our simulations exhibit the previously observed numerical instabilities of variational time propagation approaches.
arXiv Detail & Related papers (2022-03-04T19:00:45Z) - Multistate Transition Dynamics by Strong Time-Dependent Perturbation in
NISQ era [0.0]
We develop a quantum computing scheme utilizing McLachlan variational principle in a hybrid quantum-classical algorithm.
Results for transition probabilities are obtained with accuracy better than 1%, as established by comparison to the benchmark data.
arXiv Detail & Related papers (2021-12-13T00:49:15Z) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - Quantum Algorithms for Solving Ordinary Differential Equations via
Classical Integration Methods [1.802439717192088]
We explore utilizing quantum computers for the purpose of solving differential equations.
We devise and simulate corresponding digital quantum circuits, and implement and run a 6$mathrmth$ order Gauss-Legendre collocation method.
As promising future scenario, the digital arithmetic method could be employed as an "oracle" within quantum search algorithms for inverse problems.
arXiv Detail & Related papers (2020-12-17T09:49:35Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - Quantum Algorithm for Smoothed Particle Hydrodynamics [0.0]
We present a quantum computing algorithm for the smoothed particle hydrodynamics (SPH) method.
Error convergence is exponentially fast in the number of qubits.
We extend the method to solve the one-dimensional advection and partial diffusion differential equations.
arXiv Detail & Related papers (2020-06-11T18:28:24Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z) - Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular
Simulations on Quantum Computing Devices [0.0]
We introduce a quantum solver of contracted eigenvalue equations, the quantum analogue of classical methods for the energies.
We demonstrate the algorithm though computations on both a quantum simulator and two IBM quantum processing units.
arXiv Detail & Related papers (2020-04-23T18:35:26Z) - Practical Quantum Computing: solving the wave equation using a quantum
approach [0.0]
We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm.
Our implementation proves experimentally that some PDE can be solved on a quantum computer.
arXiv Detail & Related papers (2020-03-27T15:05:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.