Alternatives to a nonhomogeneous partial differential equation quantum algorithm
arXiv:2205.05541 · doi:10.1103/PhysRevA.106.052431
Abstract
Recently J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form . Its nonhomogeneous solution is obtained by inverting the operator along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for a specific set of inputs. These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.
arXiv comment10 pages, 8 figures
References in corpus (22)
- Quantum Machine Learning
- Quantum algorithm for solving linear systems of equations
- Quantum principal component analysis
- Quantum computation over continuous variables
- Continuous-variable quantum neural networks
- Preconditioned quantum linear system algorithm
- High-order quantum algorithm for solving linear differential equations
- Efficient quantum algorithm for dissipative nonlinear differential equations
- Finding Exponential Product Formulas of Higher Orders
- Quantum algorithm for linear differential equations with exponentially improved dependence on precision
- Quantum algorithms and the finite element method
- High-precision quantum algorithms for partial differential equations
- Machine learning method for state preparation and gate synthesis on photonic quantum computers
- Quantum spectral methods for differential equations
- Quantum algorithm for non-homogeneous linear partial differential equations
- A Quantum Algorithm for Solving Linear Differential Equations: Theory and Experiment
- How to decompose arbitrary continuous-variable quantum operations
- Quantum Algorithms for Solving Ordinary Differential Equations via Classical Integration Methods
- Exact gate decompositions for photonic quantum computing
- Quantum Fourier analysis for multivariate functions and applications to a class of Schrödinger-type partial differential equations
- Quantum advantage for differential equation analysis
- A hybrid classical-quantum approach to solve the heat equation using quantum annealers