Quantum simulation of the Fokker-Planck equation via Schrodingerization (2404.13585v2)
Abstract: This paper studies a quantum simulation technique for solving the Fokker-Planck equation. Traditional semi-discretization methods often fail to preserve the underlying Hamiltonian dynamics and may even modify the Hamiltonian structure, particularly when incorporating boundary conditions. We address this challenge by employing the Schrodingerization method-it converts any linear partial and ordinary differential equation with non-Hermitian dynamics into systems of Schrodinger-type equations. We explore the application in two distinct forms of the Fokker-Planck equation. For the conservation form, we show that the semi-discretization-based Schrodingerization is preferable, especially when dealing with non-periodic boundary conditions. Additionally, we analyze the Schrodingerization approach for unstable systems that possess positive eigenvalues in the real part of the coefficient matrix or differential operator. Our analysis reveals that the direct use of Schrodingerization has the same effect as a stabilization procedure. For the heat equation form, we propose a quantum simulation procedure based on the time-splitting technique. We discuss the relationship between operator splitting in the Schrodingerization method and its application directly to the original problem, illustrating how the Schrodingerization method accurately reproduces the time-splitting solutions at each step. Furthermore, we explore finite difference discretizations of the heat equation form using shift operators. Utilizing Fourier bases, we diagonalize the shift operators, enabling efficient simulation in the frequency space. Providing additional guidance on implementing the diagonal unitary operators, we conduct a comparative analysis between diagonalizations in the Bell and the Fourier bases, and show that the former generally exhibits greater efficiency than the latter.
- Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost. Phys. Rev. Lett., 131(15):150603, 2023.
- Hamiltonian simulation with nearly optimal dependence on all parameters. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, pages 792–809, 2015.
- Quantum algorithm for linear differential equations with exponentially improved dependence on precision. Comm. Math. Phys., 356(3):1057–1081, 2017.
- Time-dependent Hamiltonian simulation with l1superscript𝑙1l^{1}italic_l start_POSTSUPERSCRIPT 1 end_POSTSUPERSCRIPT-norm scaling. Quantum, 4:254, 2020.
- D. W. Berry. High-order quantum algorithm for solving linear differential equations. J. Phys. A: Math. Theor., 47(10):105301, 17 pp., 2014.
- Control of quantum noise: On the role of dilations. Ann. Henri Poincaré, 24:325–347, 2023.
- Optimal scaling quantum linear systems solver via discrete adiabatic theorem. arXiv:2111.08152, 2021.
- A numerical solver for a nonlinear Fokker-Planck equation representation of neuronal network dynamics. J. Comput. Phys., 230(4):1084–1099, 2011.
- Quantum simulation for time-dependent Hamiltonians – with applications to non-autonomous ordinary and partial differential equations. arXiv:2312.02817, 2023.
- Quantum algorithm for simulating the wave equation. Phys. Rev. A, 99:012323, 22 pp., 2019.
- Quantum algorithm for systems of linear equations with exponentially improved dependence on precision. SIAM J. Comput., 46(6):1920–1950, 2017.
- A. W. Childs and J. Liu. Quantum spectral methods for differential equations. Comm. Math. Phys., 375(2):1427–1457, 2020.
- High-precision quantum algorithms for partial differential equations. Quantum, 5:574, 2021.
- Quantum algorithm and circuit design solving the Poisson equation. New J. Phys., 15:013021, 2013.
- D. F. Escande and F. Sattin. When can the Fokker-Planck equation describe anomalous or chaotic transport? Phys. Rev. Lett., 99:185005, 2007.
- Quantum algorithm for the Vlasov equation. Phys. Rev. A, 100:062315, Dec 2019.
- Simulating option price dynamics with exponential quantum speedup. arXiv:2101.04023v3, 2022.
- Quantum algorithms for uncertainty quantification: application to partial differential equations. arXiv:2209.11220, 2022.
- Quantum algorithm for linear systems of equations. Phys. Rev. Lett., 103(15):150502, 4 pp., 2009.
- Quantum circuits for partial differential equations via Schrödingerisation. arXiv:2403.10032, 2024.
- J. Hu and X. Zhang. Positivity-preserving and energy-dissipative finite difference schemes for the Fokker-Planck and Keller-Segel equations. IMA J. Numer. Anal., 43:1450–1484, 2023.
- S. Jin and N. Liu. Quantum algorithms for computing observables of nonlinear partial differential equations. arXiv:2202.07834, 2022.
- S. Jin and N. Liu. Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra via Schrödingerisation. arXiv preprint arXiv:2304.02865, 2023.
- Quantum simulation of Maxwell’s equations via Schrödingersation. arXiv:2308.08408, 2023.
- Quantum simulation in the semi-classical regime. Quantum, 6:739, 2022.
- Quantum simulation for quantum dynamics with artificial boundary conditions. arXiv: 2304.00667, 2023.
- Quantum simulation for partial differential equations with physical boundary or interface conditions. J. Comp. Phys., 498:112707, 2024.
- On Schrödingerization based quantum algorithms for linear dynamical systems with inhomogeneous terms. arXiv:2402.14696, 2024.
- Schrödingerisation based computationally stable algorithms for ill-posed problems in partial differential equations. arXiv:2403.19123, 2024.
- Quantum simulation of partial differential equations via Schrödingerisation. arXiv:2212.13969, 2022.
- Time complexity analysis of quantum difference methods for linear high dimensional and multiscale partial differential equations. J. Comput. Phys., 471:111641, 2022.
- Quantum simulation of partial differential equations: Applications and detailed analysis. Physical Review A, 108:032603, 2023.
- Time complexity analysis of quantum algorithms via linear representations for nonlinear ordinary and partial differential equations. J. Comput. Phys., 487:112149, 2023.
- S. Jin and Y. Zhu. Hypocoercivity and uniform regularity for the Vlasov-Poisson-Fokker-Planck system with uncertainty and multiple scales. SIAM J. Math. Anal., 50:1790–1816, 2018.
- Polynomial-time quantum algorithm for the simulation of chemical dynamics. Proceedings of the National Academy of Sciences, 105(48):18681–18686, 2008.
- L. Lin. Lecture notes on quantum algorithms for scientific computation. arXiv:2201.08309, 2022.
- Quantum vs. classical algorithms for solving the heat equation. arXiv:2004.06516, 2020.
- H. P. McKean, Jr. A class of Markov processes associated with nonlinear parabolic equations. Proc. Nat. Acad. Sci. U.S.A., 56:1907–1911, 1966.
- A. Montanaro and S. Pallister. Quantum algorithms and the finite element method. Phys. Rev. A, 93:032324, 14 pp., 2016.
- Solving the Fokker-Planck kinetic equation on a lattice. Phys. Rev. E, 73:066707, 2006.
- P. A. Markowich and C. Villani. On the trend to equilibrium for the Fokker-Planck equation: an interplay between physics and functional analysis. volume 19, pages 1–29. 2000. VI Workshop on Partial Differential Equations, Part II (Rio de Janeiro, 1999).
- Quantum Computation and Quantum Information. Cambridge, New York, 2010.
- G. A. Pavliotis. Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations. Springer-Verlag, New York, 2014.
- H. Risken. The Fokker-Planck Equation: Methods of Solution and Applications. Springer-Verlag Berlin Heidelberg, 1989.
- Hamiltonian simulation for time-evolving partial differential equation by scalable quantum circuits. arXiv:2402.18398, 2024.
- Efficient and fail-safe collisionless quantum Boltzmann method. arXiv:2211.14269, 2022.
- Y. Subasi and R. D. Somma. Quantum algorithms for systems of linear equations inspired by adiabatic quantum computing. Phys. Rev. Lett., 122:060504, 2019.
- Numerical solution of the Fokker-Planck equation using physics-based mixture models. Math. Models Methods Appl. Sci., 399:115424, 2022.
- A Vlasov-Fokker-Planck-Landau code for the simulation of colliding supersonic dense plasma flows. J. Comp. Phys., 503:112843, 2024.