Qade: Solving Differential Equations on Quantum Annealers
- URL: http://arxiv.org/abs/2204.03657v1
- Date: Thu, 7 Apr 2022 18:00:00 GMT
- Title: Qade: Solving Differential Equations on Quantum Annealers
- Authors: Juan Carlos Criado, Michael Spannowsky
- Abstract summary: We present a general method, called Qade, for solving differential equations using a quantum annealer.
On current devices, Qade can solve systems of coupled partial differential equations that depend linearly on the solution and its derivatives.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a general method, called Qade, for solving differential equations
using a quantum annealer. The solution is obtained as a linear combination of a
set of basis functions. On current devices, Qade can solve systems of coupled
partial differential equations that depend linearly on the solution and its
derivatives, with non-linear variable coefficients and arbitrary inhomogeneous
terms. We test the method with several examples and find that state-of-the-art
quantum annealers can find the solution accurately for problems requiring a
small enough function basis. We provide a Python package implementing the
method at gitlab.com/jccriado/qade.
Related papers
- MultiSTOP: Solving Functional Equations with Reinforcement Learning [56.073581097785016]
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics.
This new methodology produces actual numerical solutions instead of bounds on them.
arXiv Detail & Related papers (2024-04-23T10:51:31Z) - Physics-Informed Quantum Machine Learning: Solving nonlinear
differential equations in latent spaces without costly grid evaluations [21.24186888129542]
We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional differential equations.
By measuring the overlaps between states which are representations of DE terms, we construct a loss that does not require independent sequential function evaluations on grid points.
When the loss is trained variationally, our approach can be related to the differentiable quantum circuit protocol.
arXiv Detail & Related papers (2023-08-03T15:38:31Z) - Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the
Quantum Many-Body Schr\"odinger Equation [56.9919517199927]
"Wasserstein Quantum Monte Carlo" (WQMC) uses the gradient flow induced by the Wasserstein metric, rather than Fisher-Rao metric, and corresponds to transporting the probability mass, rather than teleporting it.
We demonstrate empirically that the dynamics of WQMC results in faster convergence to the ground state of molecular systems.
arXiv Detail & Related papers (2023-07-06T17:54:08Z) - Symbolic Recovery of Differential Equations: The Identifiability Problem [52.158782751264205]
Symbolic recovery of differential equations is the ambitious attempt at automating the derivation of governing equations.
We provide both necessary and sufficient conditions for a function to uniquely determine the corresponding differential equation.
We then use our results to devise numerical algorithms aiming to determine whether a function solves a differential equation uniquely.
arXiv Detail & Related papers (2022-10-15T17:32:49Z) - Automated differential equation solver based on the parametric
approximation optimization [77.34726150561087]
The article presents a method that uses an optimization algorithm to obtain a solution using the parameterized approximation.
It allows solving the wide class of equations in an automated manner without the algorithm's parameters change.
arXiv Detail & Related papers (2022-05-11T10:06:47Z) - Quantum Kernel Methods for Solving Differential Equations [21.24186888129542]
We propose several approaches for solving differential equations (DEs) with quantum kernel methods.
We compose quantum models as weighted sums of kernel functions, where variables are encoded using feature maps and model derivatives are represented.
arXiv Detail & Related papers (2022-03-16T18:56:35Z) - Message Passing Neural PDE Solvers [60.77761603258397]
We build a neural message passing solver, replacing allally designed components in the graph with backprop-optimized neural function approximators.
We show that neural message passing solvers representationally contain some classical methods, such as finite differences, finite volumes, and WENO schemes.
We validate our method on various fluid-like flow problems, demonstrating fast, stable, and accurate performance across different domain topologies, equation parameters, discretizations, etc., in 1D and 2D.
arXiv Detail & Related papers (2022-02-07T17:47:46Z) - QBoost for regression problems: solving partial differential equations [0.0]
The hybrid algorithm is capable of finding a solution to a partial differential equation with good precision and favorable scaling in the required number of qubits.
The classical part is composed by training several regressors, capable of solving a partial differential equation using machine learning.
The quantum part consists of adapting the QBoost algorithm to solve regression problems.
arXiv Detail & Related papers (2021-08-30T16:13:04Z) - Q-Match: Iterative Shape Matching via Quantum Annealing [64.74942589569596]
Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP)
This paper proposes Q-Match, a new iterative quantum method for QAPs inspired by the alpha-expansion algorithm.
Q-Match can be applied for shape matching problems iteratively, on a subset of well-chosen correspondences, allowing us to scale to real-world problems.
arXiv Detail & Related papers (2021-05-06T17:59:38Z) - Solving Differential Equations via Continuous-Variable Quantum Computers [0.0]
We explore how a continuous-dimensional (CV) quantum computer could solve a classic differential equation, making use of its innate capability to represent real numbers in qumodes.
Our simulations and parameter optimization using the PennyLane / Strawberry Fields framework demonstrate good both linear and non-linear ODEs.
arXiv Detail & Related papers (2020-12-22T18:06:12Z) - Solving nonlinear differential equations with differentiable quantum
circuits [21.24186888129542]
We propose a quantum algorithm to solve systems of nonlinear differential equations.
We use automatic differentiation to represent function derivatives in an analytical form as differentiable quantum circuits.
We show how this approach can implement a spectral method for solving differential equations in a high-dimensional feature space.
arXiv Detail & Related papers (2020-11-20T13:21:11Z)
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.